Neural network modeling and control of proton exchange membrane fuel cell
Introduction to Artificial Intelli智慧树知到课后章节答案2023年
Introduction to Artificial Intelligence智慧树知到课后章节答案2023年下哈尔滨工程大学哈尔滨工程大学第一章测试1.All life has intelligence The following statements about intelligence arewrong（）A:All life has intelligence B:Bacteria do not have intelligence C:At present,human intelligence is the highest level of nature D:From the perspective of life, intelligence is the basic ability of life to adapt to the natural world答案:Bacteria do not have intelligence2.Which of the following techniques is unsupervised learning in artificialintelligence?（）A:Neural network B:Support vector machine C:Decision tree D:Clustering答案:Clustering3.To which period can the history of the development of artificial intelligencebe traced back?（）A:1970s B:Late 19th century C:Early 21st century D:1950s答案:Late 19th century4.Which of the following fields does not belong to the scope of artificialintelligence application?（）A:Aviation B:Medical C:Agriculture D:Finance答案:Aviation5.The first artificial neuron model in human history was the MP model,proposed by Hebb.（）A:对 B:错答案:错6.Big data will bring considerable value in government public services, medicalservices, retail, manufacturing, and personal location services. （）A:错 B:对答案:对第二章测试1.Which of the following options is not human reason:（）A:Value rationality B:Intellectual rationality C:Methodological rationalityD:Cognitive rationality答案:Intellectual rationality2.When did life begin? （）A:Between 10 billion and 4.5 billion years B:Between 13.8 billion years and10 billion years C:Between 4.5 billion and 3.5 billion years D:Before 13.8billion years答案:Between 4.5 billion and 3.5 billion years3.Which of the following statements is true regarding the philosophicalthinking about artificial intelligence?（）A:Philosophical thinking has hindered the progress of artificial intelligence.B:Philosophical thinking has contributed to the development of artificialintelligence. C:Philosophical thinking is only concerned with the ethicalimplications of artificial intelligence. D:Philosophical thinking has no impact on the development of artificial intelligence.答案:Philosophical thinking has contributed to the development ofartificial intelligence.4.What is the rational nature of artificial intelligence?（）A:The ability to communicate effectively with humans. B:The ability to feel emotions and express creativity. C:The ability to reason and make logicaldeductions. D:The ability to learn from experience and adapt to newsituations.答案:The ability to reason and make logical deductions.5.Which of the following statements is true regarding the rational nature ofartificial intelligence?（）A:The rational nature of artificial intelligence includes emotional intelligence.B:The rational nature of artificial intelligence is limited to logical reasoning.C:The rational nature of artificial intelligence is not important for itsdevelopment. D:The rational nature of artificial intelligence is only concerned with mathematical calculations.答案:The rational nature of artificial intelligence is limited to logicalreasoning.6.Connectionism believes that the basic element of human thinking is symbol,not neuron; Human's cognitive process is a self-organization process ofsymbol operation rather than weight. （）A:对 B:错答案:错第三章测试1.The brain of all organisms can be divided into three primitive parts:forebrain, midbrain and hindbrain. Specifically, the human brain is composed of brainstem, cerebellum and brain (forebrain). （）A:错 B:对答案:对2.The neural connections in the brain are chaotic. （）A:对 B:错答案:错3.The following statement about the left and right half of the brain and itsfunction is wrong （）.A:When dictating questions, the left brain is responsible for logical thinking,and the right brain is responsible for language description. B:The left brain is like a scientist, good at abstract thinking and complex calculation, but lacking rich emotion. C:The right brain is like an artist, creative in music, art andother artistic activities, and rich in emotion D:The left and right hemispheres of the brain have the same shape, but their functions are quite different. They are generally called the left brain and the right brain respectively.答案:When dictating questions, the left brain is responsible for logicalthinking, and the right brain is responsible for language description.4.What is the basic unit of the nervous system?（）A:Neuron B:Gene C:Atom D:Molecule答案:Neuron5.What is the role of the prefrontal cortex in cognitive functions?（）A:It is responsible for sensory processing. B:It is involved in emotionalprocessing. C:It is responsible for higher-level cognitive functions. D:It isinvolved in motor control.答案:It is responsible for higher-level cognitive functions.6.What is the definition of intelligence?（）A:The ability to communicate effectively. B:The ability to perform physicaltasks. C:The ability to acquire and apply knowledge and skills. D:The abilityto regulate emotions.答案:The ability to acquire and apply knowledge and skills.第四章测试1.The forward propagation neural network is based on the mathematicalmodel of neurons and is composed of neurons connected together by specific connection methods. Different artificial neural networks generally havedifferent structures, but the basis is still the mathematical model of neurons.（）A:对 B:错答案:对2.In the perceptron, the weights are adjusted by learning so that the networkcan get the desired output for any input. （）A:对 B:错答案:对3.Convolution neural network is a feedforward neural network, which hasmany advantages and has excellent performance for large image processing.Among the following options, the advantage of convolution neural network is（）.A:Implicit learning avoids explicit feature extraction B:Weight sharingC:Translation invariance D:Strong robustness答案:Implicit learning avoids explicit feature extraction;Weightsharing;Strong robustness4.In a feedforward neural network, information travels in which direction?（）A:Forward B:Both A and B C:None of the above D:Backward答案:Forward5.What is the main feature of a convolutional neural network?（）A:They are used for speech recognition. B:They are used for natural languageprocessing. C:They are used for reinforcement learning. D:They are used forimage recognition.答案:They are used for image recognition.6.Which of the following is a characteristic of deep neural networks?（）A:They require less training data than shallow neural networks. B:They havefewer hidden layers than shallow neural networks. C:They have loweraccuracy than shallow neural networks. D:They are more computationallyexpensive than shallow neural networks.答案:They are more computationally expensive than shallow neuralnetworks.第五章测试1.Machine learning refers to how the computer simulates or realizes humanlearning behavior to obtain new knowledge or skills, and reorganizes the existing knowledge structure to continuously improve its own performance.（）A:对 B:错答案:对2.The best decision sequence of Markov decision process is solved by Bellmanequation, and the value of each state is determined not only by the current state but also by the later state.（）A:对 B:错答案:对3.Alex Net's contributions to this work include: （）.A:Use GPUNVIDIAGTX580 to reduce the training time B:Use the modified linear unit (Re LU) as the nonlinear activation function C:Cover the larger pool to avoid the average effect of average pool D:Use the Dropouttechnology to selectively ignore the single neuron during training to avoid over-fitting the model答案:Use GPUNVIDIAGTX580 to reduce the training time;Use themodified linear unit (Re LU) as the nonlinear activation function;Cover the larger pool to avoid the average effect of average pool;Use theDropout technology to selectively ignore the single neuron duringtraining to avoid over-fitting the model4.In supervised learning, what is the role of the labeled data?（）A:To evaluate the model B:To train the model C:None of the above D:To test the model答案:To train the model5.In reinforcement learning, what is the goal of the agent?（）A:To identify patterns in input data B:To minimize the error between thepredicted and actual output C:To maximize the reward obtained from theenvironment D:To classify input data into different categories答案:To maximize the reward obtained from the environment6.Which of the following is a characteristic of transfer learning?（）A:It can only be used for supervised learning tasks B:It requires a largeamount of labeled data C:It involves transferring knowledge from onedomain to another D:It is only applicable to small-scale problems答案:It involves transferring knowledge from one domain to another第六章测试1.Image segmentation is the technology and process of dividing an image intoseveral specific regions with unique properties and proposing objects ofinterest. In the following statement about image segmentation algorithm, the error is （）.A:Region growth method is to complete the segmentation by calculating the mean vector of the offset. B:Watershed algorithm, MeanShift segmentation,region growth and Ostu threshold segmentation can complete imagesegmentation. C:Watershed algorithm is often used to segment the objectsconnected in the image. D:Otsu threshold segmentation, also known as themaximum between-class difference method, realizes the automatic selection of global threshold T by counting the histogram characteristics of the entire image答案:Region growth method is to complete the segmentation bycalculating the mean vector of the offset.2.Camera calibration is a key step when using machine vision to measureobjects. Its calibration accuracy will directly affect the measurementaccuracy. Among them, camera calibration generally involves the mutualconversion of object point coordinates in several coordinate systems. So,what coordinate systems do you mean by "several coordinate systems" here?（）A:Image coordinate system B:Image plane coordinate system C:Cameracoordinate system D:World coordinate system答案:Image coordinate system;Image plane coordinate system;Camera coordinate system;World coordinate systemmonly used digital image filtering methods:（）.A:bilateral filtering B:median filter C:mean filtering D:Gaussian filter答案:bilateral filtering;median filter;mean filtering;Gaussian filter4.Application areas of digital image processing include:（）A:Industrial inspection B:Biomedical Science C:Scenario simulation D:remote sensing答案:Industrial inspection;Biomedical Science5.Image segmentation is the technology and process of dividing an image intoseveral specific regions with unique properties and proposing objects ofinterest. In the following statement about image segmentation algorithm, the error is ( ).A:Otsu threshold segmentation, also known as the maximum between-class difference method, realizes the automatic selection of global threshold T by counting the histogram characteristics of the entire imageB: Watershed algorithm is often used to segment the objects connected in the image. C:Region growth method is to complete the segmentation bycalculating the mean vector of the offset. D:Watershed algorithm, MeanShift segmentation, region growth and Ostu threshold segmentation can complete image segmentation.答案:Region growth method is to complete the segmentation bycalculating the mean vector of the offset.第七章测试1.Blind search can be applied to many different search problems, but it has notbeen widely used due to its low efficiency.（）A:错 B:对答案:对2.Which of the following search methods uses a FIFO queue （）.A:width-first search B:random search C:depth-first search D:generation-test method答案:width-first search3.What causes the complexity of the semantic network （）.A:There is no recognized formal representation system B:The quantifiernetwork is inadequate C:The means of knowledge representation are diverse D:The relationship between nodes can be linear, nonlinear, or even recursive 答案:The means of knowledge representation are diverse;Therelationship between nodes can be linear, nonlinear, or even recursive4.In the knowledge graph taking Leonardo da Vinci as an example, the entity ofthe character represents a node, and the relationship between the artist and the character represents an edge. Search is the process of finding the actionsequence of an intelligent system.（）A:对 B:错答案:对5.Which of the following statements about common methods of path search iswrong（）A:When using the artificial potential field method, when there are someobstacles in any distance around the target point, it is easy to cause the path to be unreachable B:The A* algorithm occupies too much memory during the search, the search efficiency is reduced, and the optimal result cannot beguaranteed C:The artificial potential field method can quickly search for acollision-free path with strong flexibility D:A* algorithm can solve theshortest path of state space search答案:When using the artificial potential field method, when there aresome obstacles in any distance around the target point, it is easy tocause the path to be unreachable第八章测试1.The language, spoken language, written language, sign language and Pythonlanguage of human communication are all natural languages.（）A:对 B:错答案:错2.The following statement about machine translation is wrong （）.A:The analysis stage of machine translation is mainly lexical analysis andpragmatic analysis B:The essence of machine translation is the discovery and application of bilingual translation laws. C:The four stages of machinetranslation are retrieval, analysis, conversion and generation. D:At present,natural language machine translation generally takes sentences as thetranslation unit.答案:The analysis stage of machine translation is mainly lexical analysis and pragmatic analysis3.Which of the following fields does machine translation belong to? （）A:Expert system B:Machine learning C:Human sensory simulation D:Natural language system答案:Natural language system4.The following statements about language are wrong: （）。
神经网络模拟人脑的机器学习模型
03
基于值函数与策略结合的强化学习
结合值函数估计和策略优化，如Actor-Critic架构中的优势演员-评论家
（A2C）、异步优势演员-评论家（A3C）等算法。这些算法在稳定性
和收敛速度方面具有较好的性能。
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神经网络模拟人脑的 关键技术
深度学习技术
1 2 3
深度神经网络
通过构建多层的神经元网络，模拟人脑神经元的 复杂连接，以实现更加深入的特征学习和抽象推 理。
• 神经形态计算：未来的神经网络模型可能会借鉴神经形态计算的思想，设计更 加接近人脑神经元和突触的计算模型，从而实现更加高效和智能的计算。同时 ，神经形态计算还可以降低模型的功耗和硬件成本，提高模型的实用性。
• 个性化定制：随着数据收集和处理技术的进步，未来的神经网络模型可能会实 现个性化定制。针对不同用户的需求和应用场景，定制专属的神经网络模型， 以提高模型的性能和用户满意度。这将有助于拓展神经网络模型的应用领域， 促进人工智能技术的普及和发展。
认知过程的理解，进一步揭示人脑的工作原理。
推动计算机科学和神经科学的交叉研究
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神经网络模拟人脑的研究将促进计算机科学和神经科学之间的
交流和合作，为两个领域的共同发展开辟新的研究方向。
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神经网络的基本原理
神经元的模型与功能
神经元模型
神经元是神经网络的基本单元，模拟了生物神经元的结构和 功能。它接收来自其他神经元的输入信号，经过加权求和和 非线性激活函数的处理，产生输出信号传递给其他神经元。
适用于序列数据的处理，通过隐藏层状态的传递，捕捉序 列中的长期依赖关系。在语音识别、自然语言处理等领域 有广泛应用。
无监督学习模型
自编码器（Autoencoder）
本人所在学科团队
本人所在学科团队【前言】本人所在学科团队是一个跨学科的研究团队，主要聚焦于机器学习、计算机视觉、自然语言处理等领域。
我们的研究目标是探索如何利用人工智能技术解决现实生活中的问题，提高人们的生活质量和工作效率。
【团队成员】我们的团队由一群来自不同背景和专业领域的研究人员组成，包括计算机科学、数学、统计学、物理学等专业。
我们有博士后、博士生、硕士生和本科生等不同层次的研究人员。
虽然大家专业背景各异，但都对人工智能技术有着浓厚的兴趣和热情，并愿意为实现这个目标而努力。
【研究方向】我们主要关注以下几个方向：1. 机器学习机器学习是我们最核心的研究方向之一。
我们致力于开发新的算法和模型来解决各种实际问题，如图像分类、自然语言处理、推荐系统等。
我们还在探索如何将深度学习技术应用于更广泛的领域，如医疗、金融等。
2. 计算机视觉计算机视觉是我们另一个重要的研究方向。
我们致力于开发新的算法和模型来解决图像处理、目标检测、人脸识别等问题。
我们还在探索如何将计算机视觉技术应用于自动驾驶、智能安防等领域。
3. 自然语言处理自然语言处理是我们的另一个研究方向。
我们致力于开发新的算法和模型来解决文本分类、情感分析、问答系统等问题。
我们还在探索如何将自然语言处理技术应用于智能客服、智能写作等领域。
【研究成果】我们的团队已经取得了一些令人骄傲的成果，包括：1. 发表多篇高水平论文我们已经在多个国际顶级会议和期刊上发表了多篇高水平论文，如CVPR、ICCV、ECCV、NIPS等。
这些论文得到了同行专家的高度评价，并对学术界和工业界产生了广泛影响。
2. 参加多个竞赛并获奖我们的团队还参加了多个国际机器学习竞赛，并获得了多个奖项，如ImageNet、COCO、Kaggle等。
这些竞赛对于验证我们的算法和模型的有效性和实用性非常重要。
3. 开源代码库我们还开源了一些高质量的代码库，如PyTorch-YOLOv3、BERT-Chinese-Text-Classification等。
《2024年基于多尺度和注意力机制融合的语义分割模型研究》范文
《基于多尺度和注意力机制融合的语义分割模型研究》篇一一、引言随着深度学习技术的不断发展，语义分割作为计算机视觉领域的一个重要任务，逐渐成为研究的热点。
语义分割旨在将图像中的每个像素划分为不同的语义类别，为图像理解提供了更加细致的信息。
然而，由于实际场景中存在多尺度目标和复杂背景的干扰，语义分割任务仍面临诸多挑战。
为了解决这些问题，本文提出了一种基于多尺度和注意力机制融合的语义分割模型。
二、相关工作语义分割作为计算机视觉的一个关键任务，在近几年的研究中得到了广泛的关注。
目前主流的语义分割模型主要采用深度卷积神经网络（CNN）来实现。
这些模型通过捕获上下文信息、提高特征表达能力等手段提高分割精度。
然而，在处理多尺度目标和复杂背景时，这些模型仍存在局限性。
为了解决这些问题，本文提出了一种融合多尺度和注意力机制的语义分割模型。
三、模型与方法本文提出的模型主要由两个部分组成：多尺度特征提取和注意力机制融合。
（一）多尺度特征提取多尺度特征提取是提高语义分割性能的关键技术之一。
在本模型中，我们采用了不同尺度的卷积核和池化操作来提取图像的多尺度特征。
具体而言，我们设计了一个包含多种尺度卷积核的卷积层，以捕获不同尺度的目标信息。
此外，我们还采用了池化操作来获取更大尺度的上下文信息。
这些多尺度特征将被用于后续的注意力机制融合。
（二）注意力机制融合注意力机制是一种有效的提高模型性能的技术，可以使得模型更加关注重要的区域。
在本模型中，我们采用了自注意力机制和交叉注意力机制来提高模型的表达能力。
自注意力机制主要用于捕获每个像素的上下文信息，而交叉注意力机制则用于融合不同尺度特征之间的信息。
具体而言，我们通过在卷积层之间引入自注意力和交叉注意力模块，使得模型能够更好地关注重要区域和提取多尺度特征。
四、实验与结果为了验证本文提出的模型的性能，我们在公开的语义分割数据集上进行了一系列实验。
实验结果表明，本文提出的模型在处理多尺度目标和复杂背景时具有更好的性能。
三轴磁通门传感器误差分析与校正
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国防科学技术大学研究生院硕士学位论文
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表 1.1 地磁站对磁力仪标定比例系数和正交性测试结果 .......................................... 3 表 2.1 基于设备的零偏标定值 .................................................................................... 12 表 2.2 X轴线性度误差 ................................................................................................. 16 表 4.1 算法估计的零偏值 ............................................................................................ 38 表 6.1 不同磁场下刻度因子温度特性 ........................................................................ 71 表 6.2 三种方法对刻度因子温度特性的逼近误差 .................................................... 74
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国防科学技术大学研究生院硕士学位论文
extended kalman filter based on vector calibration model is used to calibrate vector via simulation. Finally, temperature compensation model established, and the model is proved to be universal. In the end, some conclusions are given and some suggestions for further research are described in detail. Key Words：Three-axis fluxgate magnetometers; Total value calibration model; Vector calibration model; Temperature compensation model; Neural networks; Adaptive filter; Kalman filter
科研岗位招聘笔试题及解答(某世界500强集团)
招聘科研岗位笔试题及解答(某世界500强集团)(答案在后面)一、单项选择题（本大题有10小题，每小题2分，共20分）1、以下哪种算法是非监督学习的一种典型应用？A、决策树B、线性回归C、K-means聚类D、逻辑回归2、以下哪一项不是科研项目管理中的关键要素？A、项目的时间管理B、预算的制定与控制C、团队协作与人员管理D、营销策略3、在模型训练过程中，过拟合的现象通常发生在：A、训练初期B、训练中期C、训练后期D、训练结束时4、关于深度学习中的反向传播算法，下列描述正确的是：A、反向传播算法仅适用于浅层网络B、反向传播算法是用来优化模型参数的基本算法C、反向传播算法是用来正向传播信号的基本算法D、反向传播算法无法与梯度下降法结合使用5、科研项目管理的核心是什么？A、技术开发效率B、团队协作能力C、项目目标达成D、创新思维能力6、在实验设计中，什么是确保研究结果可重复性的关键？A、采取随机抽样B、使用复杂实验设备C、严格的实验操作规程D、确保数据收集的全面性7、在团队项目中，哪种沟通方式能够确保信息得到准确传递和理解？A、电子邮件B、口头报告C、面对面会议D、即时消息8、科学研究中，对于实验数据的处理和分析，哪种统计方法能够用于检测两组数据是否存在显著差异？A、卡方检验B、T检验C、方差分析D、回归分析9、在材料科学中，以下哪种材料被广泛用于电子元件中的绝缘层和防腐蚀保护？(A)铝 (B) 玻璃 (C) 聚四氟乙烯 (D) 钢 10、半导体材料在电子学中起着决定性作用，以下哪种半导体材料在其价带和导带之间具有最大的能量隙？(B)砷化镓 (B) 硅 (C) 锗 (D) 碳二、多项选择题（本大题有10小题，每小题4分，共40分）1、科研岗位员工在进行项目设计时，应遵循的原则有哪些？A. 创新性B. 科学性C. 可行性D. 经济性E. 规范性2、科研人员进行学术论文写作时，应注意以下哪些方面？A. 明确研究目的和意义B. 深入研究背景和现状C. 展示实验设计与方法D. 论述结果分析与讨论E. 清晰引文引用标注3、(多项选择题)在进行实验数据处理时，常用的统计方法包括哪些？A. 方差分析B. 偏差计算C. 回归分析D. 相关性分析E. 方差计算4、(多项选择题)以下哪些技术被广泛应用于现代科学研究中？A. 基因编辑技术B. 3D打印技术C. 云计算D. 物联网技术E. 深度学习5、在机器学习领域，以下哪些算法属于无监督学习？( ) A) k-means聚类B) 决策树 C) 支持向量机 D) 随机森林 E) 线性回归 F) 主成分分析6、在深度学习中，常用的卷积神经网络（CNN）结构有哪些常见的架构？( ) A) LeNet B) AlexNet C) VGG D) Inception E) LSTM F) Transformer7、以下关于科研项目管理的说法中，哪些是正确的？（）（2分）A、科研项目管理主要强调的是项目进度的控制。
人工智能英语介绍ppt课件
The field of AI has continued to grow quickly, with advantages in deep learning and other machine learning techniques leading to significant breakthroughs in areas such as image recognition, speech recognition, and natural language processing AI systems are now capable of performing complex tasks that were once thought to be the exclusive domain of humans
• Supervised Learning: Supervised learning algorithms are trained using labeled examples, such as input output pairs, and the goal is to generalize to new, unseen data Common supervised learning algorithms include linear regression, logistic regression, decision trees, and support vector machines
模拟ai英文面试题目及答案
模拟ai英文面试题目及答案模拟AI英文面试题目及答案1. 题目: What is the difference between a neural network anda deep learning model?答案: A neural network is a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. A deep learning model is a neural network with multiple layers, allowing it to learn more complex patterns and features from data.2. 题目: Explain the concept of 'overfitting' in machine learning.答案: Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, resulting in poor generalization to new, unseen data.3. 题目: What is the role of a 'bias' in an AI model?答案: Bias in an AI model refers to the systematic errors introduced by the model during the learning process. It can be due to the choice of model, the training data, or the algorithm's assumptions, and it can lead to unfair or inaccurate predictions.4. 题目: Describe the importance of data preprocessing in AI.答案: Data preprocessing is crucial in AI as it involves cleaning, transforming, and reducing the data to a suitableformat for the model to learn effectively. Proper preprocessing can significantly improve the performance of AI models by ensuring that the input data is relevant, accurate, and free from noise.5. 题目: How does reinforcement learning differ from supervised learning?答案: Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward signal. It differs from supervised learning, where the model learns from labeled data to predict outcomes based on input features.6. 题目: What is the purpose of a 'convolutional neural network' (CNN)?答案: A convolutional neural network (CNN) is a type of deep learning model that is particularly effective for processing data with a grid-like topology, such as images. CNNs use convolutional layers to automatically and adaptively learn spatial hierarchies of features from input images.7. 题目: Explain the concept of 'feature extraction' in AI.答案: Feature extraction in AI is the process of identifying and extracting relevant pieces of information from the raw data. It is a crucial step in many machine learning algorithms, as it helps to reduce the dimensionality of the data and to focus on the most informative aspects that can be used to make predictions or classifications.8. 题目: What is the significance of 'gradient descent' in training AI models?答案: Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In the context of AI, it is used to minimize the loss function of a model, thus refining the model's parameters to improve its accuracy.9. 题目: How does 'transfer learning' work in AI?答案: Transfer learning is a technique where a pre-trained model is used as the starting point for learning a new task. It leverages the knowledge gained from one problem to improve performance on a different but related problem, reducing the need for large amounts of labeled data and computational resources.10. 题目: What is the role of 'regularization' in preventing overfitting?答案: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function, which discourages overly complex models. It helps to control the model's capacity, forcing it to generalize better to new data by not fitting too closely to the training data.。
Modeling and Controller Design of an Electro-Hydraulic Actuator System
Abstract: Problem statement: Electro-hydraulic actuators are widely used in motion control application. Its valve needs to be controlled to determine direction of the motion. Mathematical modeling is a description of a system in terms of equations. It can be divided into two parts; physical modeling and system identification. The objective of this study was to obtain mathematical model of an electro-hydraulic system using system identification technique by estimating model using System Identification Toolbox in MATLAB. Approach: Experimental works were done to collect input and output data for model estimation and ARX model was chosen as model structure of the system. The best model was accepted based on the best fit criterion and residuals analysis of autocorrelation and cross correlation of the system input and output. PID controller was designed for the model through simulation in SIMULINK. The controller is tuning by Ziegler-Nichols method. The simulation work was verified by applying the controller to the real system to achieve the best performance of the system. Results: The result showed that the output of the system with controller in simulation mode and experimental works were improved and almost similar. Conclusion/Recommendations: The designed PID controller can be applied to the electro-hydraulic system either in simulation or real-time mode. The self-tuning or automatic tuning controller could be developed in future work to increase the reliability of the PID controller. Key words: Electro-hydraulic actuator, system identification, ARX model, PID controller INTRODUCTION Electro-hydraulic actuator system: Electro-Hydraulic Actuators (EHA) are highly non-linear system with uncertain dynamics in which the mathematical representation of the system cannot sufficiently represent the practical system (Wang et al., 2009). The actuator plays a vital role in manoeuvring industrial processes and manufacturing line. The electro-hydraulic actuator can use either proportional valve or servo valve. It converts electrical signal to hydraulic power (Zulfatman and Rahmat, 2009). There are electro-hydraulic valve actuators which move rotary motion valves such as ball, plug and butterfly valves through a quarter-turn or more from open to close. There are also valve actuators which move linear valves such as gate, globe, diaphragm and pinch valves by sliding a stem that controls the closure element. Usually, valve actuators are added to throttling valves which can be moved to any position as part of a control loop. Important specifications for electro-hydraulic valve actuators include actuation time, hydraulic fluid supply pressure range and acting type. Other features for these actuators include over torque protection, local position indication and integral pushbuttons and controls. The applications of electro-hydraulic actuators are important in the field of robotics, suspension systems and industrial process. This is because it can provide precise movement, high power capability, fast response characteristics and good positioning capability. MATERIALS AND METHODS In order to acquire the highest performance of the electro-hydraulic actuator, a suitable controller has to be designed. As the controller design require mathematical model of the system under control, a method of identifying the actuator need to be chosen so that the best accuracy of the model can be obtained. A model identification of electro-hydraulic position servo
建模算法（Modelingalgorithm）
建模算法（Modeling algorithm）1. Monte Carlo algorithm. The algorithm is also called random simulation algorithm, which is the algorithm to solve the problem through computer simulation. At the same time, it can verify the correctness of the model by simulation. It is almost the method that must be used in the game.2. data processing algorithms, such as data fitting, parameter estimation, interpolation, etc.. Games usually encounter a lot of data that needs to be processed, and the key to data processing is that these algorithms usually use MATLAB as a tool.3. linear programming, integer programming, multiple planning, two programming and other planning algorithms. Most of the problems in the modeling contest belong to optimization problems. In many cases, these problems can be described by mathematical programming algorithms, and usually solved by Lindo and Lingo software.4. graph theory algorithm. This algorithm can be divided into many kinds, including the shortest path, network flow, two points diagram and other algorithms, involving the graph theory problems can be solved by these methods, need to seriously prepare.5. computer algorithms, such as dynamic programming, backtracking search, divide and conquer algorithm, branch and bound algorithm. These algorithms are commonly used in the algorithm design, competition will be used in many occasions.6. non classical algorithms of optimization theory: simulated annealing algorithm, neural network algorithm, and genetic algorithm (three). These problems are used to solve some difficult optimization problems, which are very helpful for some problems, but the implementation of the algorithm is difficult and needs careful use.7. mesh algorithm and exhaustive method. Both are the most violent search algorithms have advantages, applications in many competitions, when focus on the model itself and despise the algorithm, you can use this violence program, it is best to use some advanced language as programming tool.8. some discretization methods for continuous data. Many problems are actual, data can be continuous, and the computer can only deal with discrete data, so the discrete difference, instead of differential summation instead of integral thought is very important.9. numerical analysis algorithm. If you use high-level language programming in the game, those commonly used algorithms in numerical analysis, such as equations solving, matrix calculation, function integration algorithm, you need to write additional library functions to call.10. image processing algorithm. Cup title has about a class of problems with graphics, graphics and even if the problem has nothing to do, we also will need pictures to illustrate the problem, these figures show how and how to deal with is the need to solve the problem, usually use MATLAB for processing.The following will be combined with competition issues over the years, the ten types of algorithms are described in detail.The following will be combined with competition issues over the years, the ten types of algorithms are described in detail.A detailed description of the 20 algorithms2.1 Monte Carlo algorithmMost modeling problems can not be separated from computer simulation, random simulation is one of the most common algorithms.One example is the 97 year A title, each part has its own calibration value, also have their own tolerance level, while the optimal combination scheme will face is a very complicated formula and 108 kinds of tolerance selection, it is impossible to obtain analytical solutions, then how to find the best solution? Stochastic simulation is a method to search the optimal solution in the feasible interval of each parts in accordance with the normal distribution of random selection of a calibration value and selects a tolerance value as a solution, and then through the Monte Carlo simulation algorithm of a large number of programs, from selecting a best. Another example is the last of the lottery second Q, needs to design a better solution, the first scheme depends on many complicated factors, the same can not describe for a model that can only rely on random simulation.2.2 data fitting, parameter estimation, interpolation andother algorithmsData fitting is used in many questions, many problems associated with graphics processing and fitting relationship, is an example of 98 years in the United States A title game, 3D interpolation of biological tissue sections, the 94 title in A altitude of the mountain cut paths through mountains, interpolation calculation,There is also a lot of noise, may be the "SARS" problem also need to use the data fitting algorithm, observe the trend of the data processing. Such problems in MATLAB have many ready-made functions can be called, familiar with MATLAB, these methods can be used with ease and ease.2.3 programming class problem algorithmThe competition there are many problems and mathematical programming, can be said that many of the models can be reduced to a set of inequality constraints, as some function as the objective function of the problem, meet this kind of problem solving is the key, for example, 98 years of B problem, with a lot of different type can describe clearly, to more convenient solution with Lindo and Lingo software, so the listing plan, so also need to be familiar with the two software.2.4 graph theory problem98 years, B 00 years B, 95 years of packing locks problems reflect the importance of the problem of graph theory, there are many algorithms for this problem include: Dijkstra, Floyd,Prim, Bellman-Ford, maximum flow, two points, etc.. Each algorithm should be implemented once, otherwise it will be written late in the game.2.5 problems in the design of computer algorithmsComputer algorithm design includes many contents: dynamic programming, backtracking search, divide and conquer algorithm, branch and bound. For example, 92 year B problem using branch and bound method, 97 year B problem is a typical dynamic programming problem, in addition, 98 year B problem reflects the divide and conquer algorithm. This problem is similar to the problem in the ACM programming contest. It is recommended to look at the book "computer algorithm design and analysis" (Electronic Industry Press) and other computer related books.Three non classical algorithms of 2.6 optimization theoryThe optimization theory has developed rapidly in the past ten years. The three algorithms, simulated annealing, neural network and genetic algorithm, are developing very fast. In recent years, the competition is more and more complex, not what good model for many problems we can learn, so these three kinds of algorithm many times can come in handy, for example: 97 years of simulated annealing algorithm A problem, neural network classification algorithm B problem for 00 years, 01 years as B problem this problem can also be the use of neural network, and the competition for 89 years A questions and BP algorithms, was just 86 years of proposed BP algorithm, 89 years passed, that tournament title may be abstract reflect today'scutting-edge technology. 03 years B gamma knife is a researchtopic, the current algorithm is the best genetic algorithm.2.7 mesh algorithm and exhaustive algorithmJust like the exhaustion method, the mesh method is only the exhaustive problem of the continuous problem. For example, the optimization problem in the case of N variables, then the space for picking these variables, for example, in the [a; b] interval, take M +1 points, that is, a; a+ (B-A) /M; a+2 (B-A) /M;...... B, then this cycle requires (M + 1) N times operation, so the amount of calculation is great. For example, 97 year A problem, 99 year B problem can be searched by grid method, this method is best in the operation speed is fasterIn the computer, but also to use high-level language to do, it is best not to use MATLAB as a grid, otherwise it will be long. Exhaustive method is familiar to everyone, do not say.2.8 some discretization methods for continuous dataMost of the programming of physics problems are related to this method. The physics problem reflects that we live in a continuous world. The computer can only deal with the discrete quantity, so it is necessary to discretize the continuous quantity. This method is widely used and is related to many algorithms above. In fact, the grid algorithm, the Monte Carlo algorithm and the simulated annealing use this idea.2.9 numerical analysis algorithmThis algorithm is specially designed for advanced languages.If you use MATLAB, Mathematica, you don't need to prepare, because there are many functions in numerical analysis, such as general mathematical software.2.10 image processing algorithmIn the 01 year A question, you need to read the BMP image and the 98 year A question of the American tournament. You need to know the 3D interpolation calculation,In the 03 year, the B question requires higher, not only the programming calculation, but also the processing, and the digital model paper also has many pictures to display, therefore the image processing is the key. It is important to learn MATLAB well, especially the part of image processing.。
recet原理
Recurrent Neural Networks (RNNs) and the BasicPrinciples of RecencyRecurrent Neural Networks (RNNs) are a type of artificial neural network that are designed to process sequential data, such as time series or natural language. They are particularly effective in capturing and modeling temporal dependencies in data. One key property of RNNs istheir ability to remember and utilize information from previous steps or time points in the sequence, which is crucial for tasks that require an understanding of context and recency. In this article, we will delveinto the basic principles of RNNs and how they enable the modeling of recency.1. Introduction to Recurrent Neural Networks (RNNs)An RNN is a type of neural network that introduces the concept of “recurrent connections” to capture the temporal nature of sequential data. Unlike feedforward neural networks, which process data in astrictly one-directional manner, RNNs have connections that allow information to flow not only from the input layer to the output layer but also across different time steps or iterations.At each time step, an RNN takes an input vector and produces an output vector. Additionally, it maintains a hidden state vector that serves as the memory of the network. This hidden state is updated at each time step based on the current input and the previous hidden state, allowing the network to retain information from previous steps.2. The Recurrent Connection and the Hidden StateThe recurrent connection in an RNN is what enables it to capture and utilize information from previous time steps. It connects the hidden state of the current step with the hidden state of the previous step. This connection forms a loop, allowing the network to maintain a form of memory.Mathematically, the hidden state at time step t, denoted by h(t), is calculated using the following equation:h(t) = f(W * x(t) + U * h(t-1))where x(t) is the input vector at time step t, W is the weight matrix connecting the input to the hidden state, U is the weight matrix connecting the hidden state to itself, and f is a non-linear activation function.The hidden state serves as a summary of the information processed by the network up to the current time step. It contains information not only from the current input but also from all previous inputs, allowing the network to have a notion of context and recency.3. Backpropagation Through Time (BPTT)Training an RNN involves updating the weights of the network to minimize the difference between the predicted output and the target output. This is typically done using the backpropagation algorithm, which calculates the gradients of the loss function with respect to the weights.In the case of RNNs, training involves a variant of backpropagation called “Backpropagation Through Time” (BPTT). BPTT unfolds the recurrent connections over time, treating the RNN as a deep neural network with shared weights. This allows the gradients to flow through the recurrent connections and update the weights accordingly.BPTT works by calculating the gradients at each time step and accumulating them over the entire sequence. The gradients are then used to update the weights using an optimization algorithm such as gradient descent. By iteratively adjusting the weights based on the accumulated gradients, the network learns to better model the temporal dependencies in the data.4. Modeling Recency with RNNsThe ability of RNNs to capture and utilize information from previous time steps makes them well-suited for tasks that require an understanding of recency. By maintaining a hidden state that retains information from past inputs, RNNs can effectively model the context and dependencies in sequential data.For example, in natural language processing tasks such as language modeling or machine translation, the meaning of a word or phrase often depends on the words that came before it. RNNs can capture these dependencies by using the hidden state to remember the context of previous words and incorporate it into the prediction of the current word.Similarly, in time series analysis, the value of a variable at a given time point is often influenced by its previous values. RNNs can learn to model these dependencies by considering the hidden state, which contains information about the past values of the variable.Overall, the ability of RNNs to model recency allows them to capture the dynamics and temporal dependencies in sequential data, making them a powerful tool in various domains.5. Variants and Extensions of RNNsWhile basic RNNs are capable of modeling recency, they suffer from certain limitations. One major issue is the vanishing gradient problem, where the gradients diminish exponentially as they propagate through time. This makes it difficult for the network to learn long-term dependencies.To overcome this problem, several variants and extensions of RNNs have been proposed. One popular variant is the Long Short-Term Memory (LSTM) network, which introduces additional gating mechanisms to control the flow of information in and out of the hidden state. LSTMs are better able to capture long-term dependencies and have been widely used in tasks such as speech recognition and sentiment analysis.Another extension is the Gated Recurrent Unit (GRU), which simplifies the LSTM architecture by combining the forget and input gates into a single update gate. GRUs have similar capabilities to LSTMs but with fewer parameters, making them computationally more efficient.These variants and extensions of RNNs have further improved theirability to model recency and capture complex dependencies in sequential data.6. ConclusionRecurrent Neural Networks (RNNs) are a class of neural networks that excel at modeling sequential data by utilizing recurrent connections and hidden states. The recurrent connection allows the network to retain information from previous time steps, enabling it to capture context and recency. Through the Backpropagation Through Time (BPTT) algorithm, RNNs can be trained to learn the temporal dependencies in the data.The ability of RNNs to model recency makes them well-suited for tasks that involve sequential data, such as natural language processing andtime series analysis. Various variants and extensions of RNNs, such as LSTMs and GRUs, have been developed to address the limitations of basic RNNs and further enhance their modeling capabilities.Overall, RNNs and their principles provide a powerful framework for understanding and modeling recency in sequential data, opening up possibilities for advancements in a wide range of fields.。
基于生成式对抗网络的画作图像合成方法
收稿日期：２０２０ ０３ １４；修回日期：２０２０ ０５ ０６ 基金项目：国家自然科学基金资助项目（９１７４６１０７） 作者简介：赵宇欣（１９９５ ），女，山西晋中人，硕士研究生，主要研究方向为机器学习、深度学习、计算机视觉（ｚｈａｏｙｕｘｉｎ＿ａｌｉｃｅ＠ｔｊｕ．ｅｄｕ．ｃｎ）；王冠（１９９２ ），女，内蒙古呼伦贝尔人，博士研究生，主要研究方向为深度学习、数学物理反问题．基于生成式对抗网络的画作图像合成方法赵宇欣，王 冠（天津大学数学学院，天津３００３５４）摘 要：画作图像合成旨在将两个不同来源的图像分别作为前景和背景融合在一起，这通常需要局部风格迁移。
现有算法过程繁琐且耗时，不能做到实时的图像合成。
针对这一缺点，提出了基于生成式对抗网络（ｇｅｎｅｒａｔｉｖｅａｄｖｅｒｓａｒｉａｌｎｅｔ，ＧＡＮ）的前向生成模型（ＰａｉｎｔｅｒＧＡＮ）。
ＰａｉｎｔｅｒＧＡＮ的自注意力机制和Ｕ Ｎｅｔ结构控制合成过程中前景的语义内容不变。
同时，对抗学习保证逼真的风格迁移。
在实验中，使用预训练模型作为ＰａｉｎｔｅｒＧＡＮ的生成器，极大地节省了计算时间和成本。
实验结果表明，比起已有方法，ＰａｉｎｔｅｒＧＡＮ生成了质量相近甚至更好的图像，生成速度也提升了４００倍，在解决局部风格迁移问题上是高质量、高效率的。
关键词：图像风格迁移；生成对抗网络；图像合成；自注意力机制中图分类号：ＴＰ３９１ ４１ 文献标志码：Ａ 文章编号：１００１ ３６９５（２０２１）０４ ０４７ １２０８ ０４ｄｏｉ：１０．１９７３４／ｊ．ｉｓｓｎ．１００１ ３６９５．２０２０．０３．００８２ＰａｉｎｔｅｒｌｙｉｍａｇｅｃｏｍｐｏｓｉｔｉｏｎｂａｓｅｄｏｎｇｅｎｅｒａｔｉｖｅａｄｖｅｒｓａｒｉａｌｎｅｔＺｈａｏＹｕｘｉｎ，ＷａｎｇＧｕａｎ（ＳｃｈｏｏｌｏｆＭａｔｈｅｍａｔｉｃｓ，ＴｉａｎｊｉｎＵｎｉｖｅｒｓｉｔｙ，Ｔｉａｎｊｉｎ３００３５４，Ｃｈｉｎａ）Ａｂｓｔｒａｃｔ：Ｐａｉｎｔｅｒｌｙｉｍａｇｅｃｏｍｐｏｓｉｔｉｎｇａｉｍｓｔｏｈａｒｍｏｎｉｚｅａｆｏｒｅｇｒｏｕｎｄｉｍａｇｅｉｎｓｅｒｔｅｄｉｎｔｏａｂａｃｋｇｒｏｕｎｄｐａｉｎｔｉｎｇ，ｗｈｉｃｈｉｓｄｏｎｅｂｙｌｏｃａｌｓｔｙｌｅｔｒａｎｓｆｅｒ．Ｔｈｅｃｈｉｅｆｄｒａｗｂａｃｋｏｆｔｈｅｅｘｉｓｔｉｎｇｍｅｔｈｏｄｓｉｓｔｈｅｈｉｇｈｃｏｍｐｕｔａｔｉｏｎａｌｃｏｓｔ，ｗｈｉｃｈｍａｋｅｓｒｅａｌ ｔｉｍｅｏｐｅｒａｔｉｏｎｄｉｆｆｉｃｕｌｔ．Ｔｏｏｖｅｒｃｏｍｅｔｈｉｓｄｒａｗｂａｃｋ，ｔｈｉｓｐａｐｅｒｐｒｏｐｏｓｅｄａｆｅｅｄ ｆｏｒｗａｒｄｍｏｄｅｌｂａｓｅｄｏｎｇｅｎｅｒａｔｉｖｅａｄｖｅｒｓａｒｉａｌｎｅｔ ｗｏｒｋ（ＧＡＮ），ｃａｌｌｅｄＰａｉｎｔｅｒＧＡＮ．ＰａｉｎｔｅｒＧＡＮｉｎｔｒｏｄｕｃｅｄａｓｅｌｆ ａｔｔｅｎｔｉｏｎｎｅｔｗｏｒｋａｎｄａＵ Ｎｅｔｔｏｃｏｎｔｒｏｌｔｈｅｓｅｍａｎｔｉｃｃｏｎｔｅｎｔｉｎｔｈｅｇｅｎｅｒａｔｅｄｉｍａｇｅ．Ｍｅａｎｗｈｉｌｅ，ａｄｖｅｒｓａｒｉａｌｌｅａｒｎｉｎｇｇｕａｒａｎｔｅｅｄａｆａｉｔｈｆｕｌｔｒａｎｓｆｅｒｏｆｓｔｙｌｅ．ＰａｉｎｔｅｒＧＡＮａｌｓｏｉｎｔｒｏｄｕｃｅｄａｐｒｅ ｔｒａｉｎｅｄｎｅｔｗｏｒｋｗｉｔｈｉｎｔｈｅｇｅｎｅｒａｔｏｒｔｏｅｘｔｒａｃｔｆｅａｔｕｒｅｓ．ＴｈｉｓａｌｌｏｗｅｄＰａｉｎｔｅｒＧＡＮｔｏｄｒａｍａｔｉｃａｌｌｙｒｅｄｕｃｅｔｒａｉｎｉｎｇ ｔｉｍｅａｎｄｓｔｏｒａｇｅ．Ｅｘｐｅｒｉｍｅｎｔｓｓｈｏｗｔｈａｔ，ｃｏｍｐａｒｅｄｔｏｓｔａｔｅ ｏｆ ａｒｔｍｅｔｈｏｄｓ，ＰａｉｎｔｅｒＧＡＮｇｅｎｅｒａｔｅｄｉｍａｇｅｓｈｕｎｄｒｅｄｓｏｆｔｉｍｅｓｆａｓｔｅｒｗｉｔｈｃｏｍｐａｒａｂｌｅｏｒｓｕｐｅｒｉｏｒｑｕａｌｉｔｙ．Ｔｈｅｒｅｆｏｒｅ，ｉｔｉｓｅｆｆｅｃｔｉｖｅａｎｄｅｆｆｉｃｉｅｎｔｆｏｒｌｏｃａｌｓｔｙｌｅｔｒａｎｓｆｅｒ．Ｋｅｙｗｏｒｄｓ：ｉｍａｇｅｓｔｙｌｅｔｒａｎｓｆｅｒ；ＧＡＮ；ｉｍａｇｅｃｏｍｐｏｓｉｔｉｎｇ；ｓｅｌｆ ａｔｔｅｎｔｉｏｎ０ 引言图像合成属于图像变换问题，目的是通过模型将一个简单的粘贴合成图像转变成一个融合为一体的图像。
Hinton大师解析神经网络(neural_network)、信念网络(belief_net)、玻尔兹曼机(RBM)
• The main problem is distinguishing true structure
from noise.
• The main problem is figuring out how to represent the complicated structure in a way that can be learned.
Typical Statistics------------Artificial Intelligence
• Low-dimensional data (e.g. less than 100 dimensions)
• Lots of noise in the data
• There is not much structure in the data, and what structure there is, can be represented by a fairly simple model.
• Keep the efficiency and simplicity of using a gradient method for adjusting the weights, but use it for modeling the structure of the sensory input. – Adjust the weights to maximize the probability that a generative model would have produced the sensory input. – Learn p(image) not p(label | image)
COMPUTER ANIMATION AND VIRTUAL WORLDS
COMPUTER ANIMATION AND VIRTUAL WORLDSComp.Anim.Virtual Worlds 2004;15:95–108(DOI:10.1002/cav.8)******************************************************************************************************Fast and learnable behavioral andcognitive modeling for virtual character animationBy Jonathan Dinerstein*,Parris K.Egbert,Hugo de Garis and Nelson Dinerstein************************************************************************************Behavioral and cognitive modeling for virtual characters is a promising field.It significantly reduces the workload on the animator,allowing characters to act autonomously in abelievable fashion.It also makes interactivity between humans and virtual characters more practical than ever before.In this paper we present a novel technique where an artificial neural network is used to approximate a cognitive model.This allows us to execute the model much more quickly,making cognitively empowered characters more practical for interactive applications.Through this approach,we can animate several thousand intelligent characters in real time on a PC.We also present a novel technique for how a virtual character,instead of using an explicit model supplied by the user,can automatically learn an unknown behavioral/cognitive model by itself through reinforcement learning.The ability to learn without an explicit model appears promising for helping behavioral and cognitive modeling become more broadly accepted and used in the computer graphics community,as it can further reduce the workload on the animator.Further,it provides solutions for problems that cannot easily be modeled explicitly.Copyright #2004John Wiley &Sons,Ltd.Received:May 2003;Revised:September 2003KEY WORDS :computer animation;synthetic characters;behavioral modeling;cognitivemodeling;machine learning;reinforcement learningIntroductionVirtual characters are an important part of computer graphics.These characters have taken forms such as synthetic humans,animals,mythological creatures,and non-organic objects that exhibit lifelike properties (walking lamps,etc).Their uses include entertainment,training,and simulation.As computing and rendering power continue to increase,virtual characters will only become more commonplace and important.One of the fundamental challenges involved in using virtual characters is animating them.It can often be difficult and time consuming to explicitly define all aspects of the behavior and animation of a complex virtual character.Further,the desired behavior may be impossible to define ahead of time if the character’s virtual world changes in unexpected or diverse ways.For these reasons,it is desirable to make virtual char-acters as autonomous and intelligent as possible while still maintaining animator control over their high-level goals.This can be accomplished with a behavioral model :an executable model defining how the character should react to stimuli from its environment.Alternatively,we can use a cognitive model :an executable model of the character’s thought process.A behavioral model is reactive (i.e.,seeks to fulfill immediate goals),whereas a cognitive model seeks to accomplish long-term goals through planning :a search for what actions should be performed in what order to reach a goal state.Thus a cognitive model is generally considered more powerful than a behavioral one,but can require significantly more processing power.As can be seen,behavioral and cognitive modeling have unique strengths and weak-nesses,and each has proven to be very useful for virtual character animation.However,despite the success of these techniques in certain domains,some important arguments have been brought against current behavioral and cognitive mod-eling systems for autonomous characters in computer graphics.******************************************************************************************************Copyright #2004John Wiley &Sons,Ltd.*Correspondence to:Jonathan Dinerstein,Brigham Young University,3366TMCB,Provo,UT 84602,USA.E-mail:jondinerstein@First,cognitive models are traditionally very slow to execute,as a tree search must be performed to formulate a plan.This speed bottleneck requires the character to make suboptimal decisions and limits the number of virtual characters that can be used simultaneously in real time.Also,since a search of all candidate actions throughout time is performed,it is necessary to use only a small set of candidate actions(which is not practical for all problems,especially those with continuous action spaces).Note that behavioral models are currently more popular than cognitive models,partially because they are usually significantly faster to execute.Second,for some problems,it can be very difficult and time consuming to construct explicit behavioral or cog-nitive models(this is known as the curse of modeling in the artificial intelligencefield).For example,it is not uncommon for behavioral/cognitive models to require weeks to design and program.Therefore,it would be extremely beneficial to have virtual characters be able to automatically learn behavioral and cognitive models if possible,alleviating the animator of this task.In this paper,we present two novel techniques.In the first technique,an artificial neural network is used to approximate a cognitive model.This allows us to exe-cute our cognitive model much more quickly,making intelligent characters more practical for interactive ap-plications.Through this approach,we can animate several thousand intelligent characters in real time on a PC.Further,this approach allows us to use optimal plans rather than suboptimal plans.The second technique we introduce allows a virtual character to automatically learn an unknown behavioral or cognitive model through reinforcement learning.The ability to learn without an explicit model appears pro-mising for helping behavioral and cognitive modeling become more broadly used in the computer graphics community,as this can further reduce the workload on the animator.Further,it provides solutions for problems that cannot easily be modeled explicitly.In summary,this paper presents the following origi-nal contributions:*a novel technique for fast execution of a cognitivemodel using neural network approximation;*a novel technique for a virtual character to auto-matically learn an approximate behavioral or cogni-tive model by itself(we call this offline character learning).We present each of these techniques in turn.We begin by surveying related work.We then give a brief introduction to cognitive modeling(as it is less well known than behavioral modeling)and neural networks.Next we present our technique for using neural networks to ra-pidly approximate cognitive models.We then give a brief introduction to reinforcement learning,and then present our technique for offline character learning.Next we present our experience with several experimental appli-cations and the lessons learned.Finally,we conclude with a summary and possible directions for future work.Related W orkPrevious computer graphics research in the area of autonomous virtual characters includes automatic gen-eration of motion primitives.1–7This is useful for redu-cing the work required by animators.More recently, Faloutsos et al.8present a technique for learning the preconditions from which a given specialist controller can succeed at its task,thus allowing them to be combined into a general-purpose motor system for physically based animated characters.Note that these approaches to motor learning focus on learning how to move to minimize a cost function(such as the energy used). Therefore,these techniques do not embody the virtual characters with any decision-making abilities.However, these techniques can be used in a complementary way with behavioral/cognitive modeling in a multilevel animation system.In other words,a behavioral/cogni-tive model makes a high-level decision for the character (e.g.,‘walk left’),which is then carried out by a lower-level animation system(e.g.,skeletal animation).A great deal of research has also been performed in control of animated autonomous characters.9–12These techniques have produced impressive results,but are limited in two aspects.First,they have no ability to learn,and therefore are limited to explicit prespecified behavior.Secondly,they only perform behavioral con-trol,not cognitive control(where behavioral means re-active decision making and cognitive means reasoning and planning to accomplish long-term tasks).Online behavioral learning has only begun to be explored in computer graphics.13–15A notable example is Blumberg et al.,16where a virtual dog can be interactively taught by the user to exhibit desired behavior.This technique is based on reinforcement learning and has been shown to work extremely well.However,it has no support for long-term reasoning to accomplish complex tasks.Also, since these learning techniques are all designed to be used online,they are(for the sake of interactive speed) limited in terms of how much they can learn.To endow virtual characters with long-term reason-ing,cognitive modeling for computer graphicswasJ.DINERSTEIN ET AL.************************************************************************************************************************************************************************************************************ Copyright#2004John Wiley&Sons,Ltd.96Comp.Anim.Virtual Worlds2004;15:95–108recently introduced.17Cognitive modeling can provide a virtual character with enough intelligence to automa-tically perform long-term,complex tasks in a believable manner.The techniques we present in this paper build on the successes of traditional behavioral and cognitive modeling with the goal of alleviating two important weaknesses:performance of cognitive models,and time-consuming construction of explicit behavioral and cognitive models.We will first present our technique for speeding up cognitive model execution through approx-imation.We will briefly review cognitive modeling and neural networks,and then present our new technique.Introduction to Cognitive ModelingCognitive modeling 17–20is closely related to behavioral modeling,but is less well known,so we now provide a brief introduction.A cognitive model defines what a character knows,how that knowledge is acquired,and how it can be used to plan actions.The traditional approach to cognitive modeling is a symbolic approach.It uses a type of first-order logic known as ‘the situation calculus’,wherein the virtual world is seen as a se-quence of situations,each of which is a ‘snapshot’of the state of the world.The most important component of a cognitive model is planning.Planning is the task of formulating a se-quence of actions that are expected to achieve a goal.Planning is performed through a tree search of all candidate actions throughout time (see Figure 1).How-ever,it is usually cost prohibitive to plan all the way to the goal state.Therefore,any given plan is usually only a partial path to the goal state,with new partial plans formulated later on.The animator has high-level control over the virtual character since she can supply it with a goal state.Note that to achieve real-time performance it is necessary to have the goal hard-coded into the cognitive model.This is because it is necessary to implement custom heuristics to speed up the tree search for planning (for further details see Funge et al.17).Therefore,either an animator and programmer must collaborate,or the programmer must also be the animator.This traditional symbolic approach to cognitive model-ing has many important strengths.It is explicit,has formal semantics,and is both human readable and executable.It also has a firm mathematical foundation and is well established in Al theory.However,it also has somesignificant weaknesses with respect to application in computer graphics animation.Since planning is per-formed through a tree search,and the branching factor is the number of actions to consider,the set of candidate actions must be kept very small if real-time performance is to be achieved.Also,to keep real-time performance,we are limited to short (suboptimal)plans.Another performance problem that is unique to computer graphics is the fact that the user may want to have many intelligent virtual characters interacting in real time.In most situa-tions,on a commodity PC,this is impossible to achieve with the traditional symbolic approach to planning.An-other limitation is that it is not possible to have a virtual character automatically learn a cognitive model by itself (which could further reduce the workload on the anima-tor,and provide solutions to very difficult problems).Introduction to Artif|cialNeural NetworksNote that there are many machine learning techniques,many of which could be used to approximate an explicit cognitive model.However,we have chosen to use neural networks because they are both compact and computationally efficient.In this section we briefly review a common type of artificial neural network.22A more thorough introduction can be found in Grzeszczuk et al.5There are many libraries and applications publicly available*(free and commercial)for constructing and executing artificial neuralnets.Figure 1.Planning is performed with a tree search of all candidate actions throughout time.To perform planning in real time without dedicated hardware,it is usually necessary to greatly limit the number of candidate actions and to onlyformulate short (suboptimal)plans.*For example,SNNS (rmatik.uni-tuebingen.de/pub/SNNS)and Xerion (/pub/xerion).FAST AND LEARNABLE BEHAVIORAL AND COGNITIVE MODELING************************************************************************************************************************************************************************************************************Copyright #2004John Wiley &Sons,Ltd.97Comp.Anim.Virtual Worlds 2004;15:95–108A neuron can be modeled as a mathematical operator that maps R p !R .Consider Figure 2(a).Neuron j re-ceives p input signals (denoted s i ).These signals are scaled by associated connection weights w ij .The neuron sums its input signalsz j ¼w 0j þX p i ¼1s i w ij ¼u Áw jwhere u ¼½1;s 1;s 2;...;s p is the input vector and w j ¼½w 0j ;w 1j ;...;w pj is the connection weight vector.The neuron outputs a signal s j ¼g ðz j Þ,where g is an activation function:s j ¼g ðz j Þ¼1=ð1þe Àz j ÞA feedforward artificial neural network (see Figure 2b),also known simply as a neural net,is a set of interconnected neurons organized in yer l receives inputs only from the neurons of layer l À1.The first layer of neurons is the input layer and the last layer is the output layer .The intermediate layers are called hidden layers .Note that the input layer has no functionality,as its neurons are simply ‘containers’for the network inputs.A neural network ‘learns’by adjusting its connection weights such that it can perform a desired computa-tional task.This involves considering input–output ex-amples of the desired functionality (or target function ).The standard approach to training a neural net is the backpropagation training algorithm.23Note that it has been proven that neural networks are universal function approximators (see Hornik et al.24).An alternative approach that we considered was to use the continuous k-nearest neighbor algorithm.21Unlike neural nets,k -nearest neighbor provides a local approx-imation of the target function,and can be used auto-matically without the user carefully selecting inputs.Also,k -nearest neighbor is guaranteed to correctly re-produce the examples that it has been provided (whereas no such guarantee exists with neural nets).However,k -nearest neighbor requires the explicit sto-rage of many examples of the target function.Becauseof this storage issue,we opted to use a neural net approach.Fast Animation Using Neural Network Approximation ofCognitive ModelsThe novel technique we now present is analogous to how a human becomes an expert at a task.As an example,let’s consider typing on a computer keyboard.When a person first learns how to type,she must search the keyboard with her eyes to find every key she wishes to press.However,after enough experience,she learns (i.e.,memorizes)where the keys are.Thereafter,she can type more quickly,only having to recall where the keys are.There is a strong parallel between this example and all other tasks humans perform.After enough experi-ence we no longer have to implicitly ‘plan’or ‘search’for our actions;we simply recall what to do.In our technique,we use a neural net to learn (i.e.,memorize)the decisions made through planning by a cognitive model to achieve a goal.Thereafter,we can quickly recall these decisions by executing the trained neural net.Training is done offline and then the trained network is used online.Thus,we can achieve intelligent virtual characters in real time using very few CPU cycles.We now present our technique in detail,first discuss-ing the structure of our technique,followed by how to train the neural network,and then finally how to use the trained network in practice.StructureA cognitive model with a goal defines a policy .A policy specifies what action to perform for a given state.A policy is formulated asa ¼ ðiÞFigure 2.(a)Mathematical model of a neuron j.(b)A three-layer feedforward neural network of p inputs and q outputs.J.DINERSTEIN ET AL.************************************************************************************************************************************************************************************************************Copyright #2004John Wiley &Sons,Ltd.98Comp.Anim.Virtual Worlds 2004;15:95–108where i is the current state and a is the action to perform.This is a non-context-sensitive formulation,which cov-ers most cognitive models.However,if desired,context information can also be supplied as input (e.g.,the last n actions can be input).We train our feed-forward neural net to approximate a specific policy .We denote theneural net approximation of the policy ^(see Figure 3a).Note that the current state (network input)and action (output)will likely be vector-valued for non-trivial virtual worlds and characters.Further,a logical selec-tion and organization of the input and output compo-nents can help make the target function as smooth as possible (and therefore easier to approximate).Selecting network inputs will be discussed in more detail later.Also note that the input should be normalized and the output denormalized for use.Specifically,the normal-ized input components should have zero means and unit variances,and the normalized output components should have 0.5means and be in the range [0.1,0.9].This ensures that all inputs contribute equivalently,and that the output is in a range the neural net’s activation function can produce.An important question is how many hidden layers (and how many neurons in each of those hidden layers)we need to use in a neural net to achieve a good approximation of a policy.This is important because we want a reasonable approximation,but we also want the neural net to be as fast to execute as possible (i.e.,there is a speed/quality trade-off).We have found that,at minimum,it is best to use one hidden layer with the same number of neurons as there are inputs.If a higher-quality approximation is desired,then it is useful to use two hidden layers,the first with 2p þ1neurons (where p is the number of inputs),and the second with 2q þ1neurons (where q is the number of outputs).We have found that any more layers and/or neurons than this usually provides little benefit.Note that the state and action spaces can be contin-uous or discrete,as all processing in a neural network is real-valued.If discrete outputs are desired,the real-valued outputs of the network should simply be quan-tized to predefined discrete values.Even though cognitive models (i.e.,policies)produce good animations in most cases,there are some cases in which they can appear too predictable.This is due to the fact that cognitive models are fundamentally determi-nistic (mapping states to actions).We now introduce an alternative form of our technique that addresses this problem.First note that,in some cases,it may be interesting to not always perform the same action for a given state (even if that action is most desirable).Occa-sional slight randomness in the decision making of an intelligent virtual character,performed in the right manner,can dramatically improve the aesthetic quality of an animation when predictability cannot be tolerated.However,it is not enough to simply choose actions at random,as this makes the virtual character appear very unintelligent.Instead,we do this in a much more believable fashion with a modification of the structure of our technique (see Figure 3b).We formulate it as a priority function :priority ¼P ði ;a ÞThe priority function represents the value of performing any given action a from the current state i under a policy .The priority can simply be an ordering of the best action to the worst,or can represent actual value in-formation (i.e.,how much an action helps the character reach a goal state).Using a priority function allows us to query for the best action at any given state,but also lets us choose an alternative action if desired (with knowl-edge of that action’s cost).For example,by using the known priorities of all candidate actions from the cur-rent state,we can select an action probabilistically.Thus our virtual character is able to make intelligent,but non-deterministic,decisions for all situations.However,note that while this non-deterministic technique is useful,we focus on standard policies in this paper.This is because they are simpler,faster,and correspond to the standard approach to cognitive modeling (i.e.,always using the best possible action in a given state).T raining the Neural NetworkWe train the neural net using the backpropagation algorithm with examples of the cognitive model’s deci-sions (i.e.,policy).A naive approach is to randomly select many examples of the entire state space.However,this is wasteful because we are usually only interested in a small portion of the state space.This is because,asaFigure 3.(a)Neural net approximation of a policy .Thenetwork input is the current state,the output is the action to perform.T and T À normalize the input and denormalize the output,respectively.(b)Neural net approximation of apriority function.FAST AND LEARNABLE BEHAVIORAL AND COGNITIVE MODELING************************************************************************************************************************************************************************************************************Copyright #2004John Wiley &Sons,Ltd.99Comp.Anim.Virtual Worlds 2004;15:95–108character makes intelligent decisions,it willfind itself traversing into only a subset of all possible states.As an example,consider a sheepdog that is herding a flock of sheep.It is illogical for the dog to become afraid of the sheep and run away.It is equally illogical for the sheep to herd the dog.Therefore,such states should never be experienced in practice.We have found that by ignoring uninteresting states the neural net’s training can focus on more important states,resulting in a higher-quality approximation.However,for the sake of robustness,it may be desirable to also use a few randomly selected states that we never expect to en-counter(to ensure that the neural net has at least seen a coarse sampling of the entire state space).To focus on the subset of the state space of interest,we generate examples by running many animations with the cognitive model.At each iteration of an animation,we have a current state and the action decided upon,which are stored for later use as training examples.We have found that using a large number of examples is best to achieve a well-generalized trained network.Specifically, we prefer to use between5000and20,000examples.Note that this is far more than is normally used when training neural nets,but we found that the use of so many examples helps to ensure that all interesting states are visited at least once(or at least a very similar state is visited).Finally,note that if a small time step is used between actions,it may be desirable to keep only an even subsampling of the examples generated through anima-tion.This is because,with a small time step,it is likely that little state change will occur with each step and therefore temporally adjacent examples may be virtually identical. We used a backpropagation learning rate of ffi0.1 and momentum of ffi0.4in all our experiments.Train-ing a neural net took about15minutes on average using a1.7GHz PC.In all of our experiments,an appropriate selection of inputs to the neural net resulted in a good approximation of a cognitive model.Choosing Salient V ariables and Features Training a neural network is not a conceptually difficult task.All that is required is to supply the backpropaga-tion algorithm with examples of the desired behavior we want the network to exhibit.However,there is one well-known challenge that we need to discuss:selecting network inputs.This is critical as too many inputs can make a neural net computationally infeasible.Also,a poor choice of inputs can be incomplete or may define a mapping that is too rough for a neural net to approx-imate well.General tips for input selection can be found in Haykin,22so we only briefly mention key points and focus our current discussion on lessons we have learned specific to approximation of cognitive models.The inputs should be salient variables(no constants), which have a strong impact in determining the answer of the function.Further,if possible,features should be used.Features are transformations or combinations of state variables.This is useful not only for reducing the total number of inputs but also for making the input–output mapping smoother.Through experience,we have discovered some useful features that we now present. When approximating cognitive models,many of the potential inputs represent raw3D geometry information (position,orientation,etc).We have found that it is very important to make all inputs rotation and translation invariant if possible.Specifically,we have found it very useful to transform all inputs so that they are relative to the local coordinate system of the virtual character.That is,rather than considering the origin to be at somefixed point in space,transform the world such that the origin is with respect to the virtual character.This not only makes it unnecessary to input the character’s current position and orientation,but also makes the mapping smoother.We have also found it useful,in some cases,to separate critical information into distinct inputs.For example,if a cognitive model relies on knowing the direction and distance to an object in its virtual world, this information could be presented as a scaled vector (dx,dy,dz).However,we have found that in many cases it is better to present this information as a normalized vector with distance(x,y,z,d),as the decision-making may be dramatically different depending on the dis-tance.In other words,if a piece of information is very important to the decision-making of a cognitive model, the mapping will likely be more smooth if that infor-mation is presented as a separate input to the neural net.Thus we need to balance the desire to keep the number of inputs low with clearly presenting all salient information.Finally,note that choosing good inputs sometimes requires experimentation to see what choice produces the best trained network,as input selection can be a dif-ficult task.However,recall that if storage is not a con-cern k-nearest neighbor can be used instead of a neural network and(as described in Mitchell21)can automati-cally discover those inputs that are necessary to approx-imate the target function.Several practical examples of selecting good inputs for neural networks to approximate cognitive models are given in the results section of thispaper.J.DINERSTEIN ET AL.************************************************************************************************************************************************************************************************************ Copyright#2004John Wiley&Sons,Ltd.100Comp.Anim.Virtual Worlds2004;15:95–108。
复杂动态网络的合作控制
:81/cnc/webpage/cooperative%20control.htm复杂动态网络的合作控制Cooperative Control of Complex Dynamic Networks⏹问题描述 Problem Description在过去的二十年中，网络和分布式计算的迅猛发展造就了从大型集成电路计算机到分布式网络工作站的一个跃变。
在工业应用中，我们期望能够应用许多价格低廉的小型设备之间的相互协调合作来替代原来造价昂贵，设计复杂的大型集成电路设备。
多智能体网络的分布式协调合作控制问题近年来引起了越来越多学者的关注，这主要归因于多智能体系统在各行各业的广泛应用，这其中包括无人驾驶飞行器的合作控制(UAVS), 形成控制(formation control), flocking, 群集(swarming), 分布式传感器网络(distributed sensor networks)，卫星的姿态控制(attitude alignment of clusters of satellites), 以及通讯网络当中的拥塞控制(congestion control).⏹典型例子 Typical Examples☐Flocking在一个多智能体系统中，所有的智能体最终能够达到速度矢量相等，相互间的距离稳定，我们称为Flocking问题。
Flocking算法最早是由Reynolds在1986年提出。
当时为了在计算中模拟Flocking，他提出了三条基本法则: (1) separation;(2) cohesion;(3) alignment。
Vicsek于1995年提出并研究了Reynolds模型的一个简化模型。
在它的模型中，所有的主体保持相同的速度运行，这个仅仅体现了Reynolds算法中的alignment。
近年来，许多控制学者也在研究Flocking问题，他们通过构建微分方程组将Flocking问题进行抽象化，利用人工势能结合速度一致(consensus)的方法来实现Flocking算法。
大数据理论考试(习题卷12)
大数据理论考试(习题卷12)说明：答案和解析在试卷最后第1部分：单项选择题，共64题，每题只有一个正确答案,多选或少选均不得分。
1.[单选题]（）试图学得一个属性的线性组合来进行预测的函数。
A)决策树B)贝叶斯分类器C)神经网络D)线性模2.[单选题]随机试验所有可能出现的结果，称为（）A)基本事件B)样本C)全部事件D)样本空间3.[单选题]DWS实例中，下列哪项不是主备配置的：A)CMSB)GTMC)OMSD)coordinato4.[单选题]数据科学家可能会同时使用多个算法（模型）进行预测，并且最后把这些算法的结果集成起来进行最后的预测（集成学习），以下对集成学习说法正确的是（）。
A)单个模型之间具有高相关性B)单个模型之间具有低相关性C)在集成学习中使用“平均权重”而不是“投票”会比较好D)单个模型都是用的一个算法5.[单选题]下面算法属于局部处理的是（）。
A)灰度线性变换B)二值化C)傅里叶变换D)中值滤6.[单选题]中文同义词替换时，常用到Word2Vec，以下说法错误的是（）。
A)Word2Vec基于概率统计B)Word2Vec结果符合当前预料环境C)Word2Vec得到的都是语义上的同义词D)Word2Vec受限于训练语料的数量和质7.[单选题]一位母亲记录了儿子3～9岁的身高，由此建立的身高与年龄的回归直线方程为y=7.19x+73.93，据此可以预测这个孩子10岁时的身高，则正确的叙述是（）。
A)身高一定是145.83cmB)身高一定超过146.00cmC)身高一定高于145.00cmD)身高在145.83cm左右8.[单选题]有关数据仓库的开发特点,不正确的描述是（）。
A)数据仓库开发要从数据出发;B)数据仓库使用的需求在开发出去就要明确;C)数据仓库的开发是一个不断循环的过程,是启发式的开发;D)在数据仓库环境中,并不存在操作型环境中所固定的和较确切的处理流,数据仓库中数据分析和处理更灵活,且没有固定的模式9.[单选题]由于不同类别的关键词对排序的贡献不同，检索算法一般把查询关键词分为几类，以下哪一类不属于此关键词类型的是（）。
面向模型未知的自由漂浮空间机械臂自适应神经鲁棒控制
WANGChao, JIN G Lijian， YEXiaoping，JIANGLihong，ZHANG Wenhui (School of Engineering,Lishui University,Lishui 323000,Zhejiang,China)
A bstract：In order to solve the problem that the precise mathematical model of free-floating space
确 获 得 ，利用神经网络控制器来补偿机械臂动力学模型, 设计网络权值的自适应学习律实现在线实时调整，避免
对 数 学 模 型 的 依 赖 .设 计 自 适 应 鲁 棒 控 制 器 来 抑 制 外 界 扰 动 和 补 偿 逼 近 误 差 ，提 高 系 统 鲁 棒 性 和 控 制 精 度 .基 于 Lyapunov理 论 ，证明了闭环系统的稳定性.仿真试验验证了所提控制方法的有效性，对于自由漂浮空间机器人 研究具有重要意义.
关 键 词 ：空 间 机 器 人 '神 经 网 络 '鲁 棒 控 制 '自 适 应 ；稳定性
中图分类号！T P 24
文献标志码： A
文 章 编 号 ！1672- 5581(2019)02-0153 - 06
Adaptive neural robust control for free-floatin* space manipulator facin* unknown model
manipulators is difficult ot obtain and the parameters of the dynamic model will change due to the external
基于深度卷积网络的运动想象脑电信号分类方法
基于深度卷积网络的运动想象脑电信号分类方法ClassificaPi os MePhod of MoPor Imagery EEG Sig sals Based os Deep Cos v oluPi onal NePwork 赵龙辉李力陈奕辉林诗柔（广东工业大学自动化学院，广东广州510006）摘要：为了提高多分类运动想象脑电信号的解码精度,以此促进脑机接口系统在生产生活中的应用。
采用基于深度卷积网络的LaNat和AlaxNat模型分析四分类运动想象脑电特性。
将脑电信号通过预处理、数据归一化和数据增强，然后分别输入两个模型中进行分类遥通过与现有不同的特征提取和分类方法对比,实验结果表明,在多分类运动想象脑电解码研究领域中，深度卷积网络模型取得的分类效果较好。
关键词:脑机接口；运动想象；深度卷积网络;脑电分类Abstract:In order to occurately extract pnd classify the EEG features of motor imagination,so os to promote th^application of the brain-computer interface system in production and life.The LeNet pnd AlexNet models based on deep convolutional networks sre used to onalyze the four-category motor imagery EEG characteristics in this paper.The EEG signals sre preprocessed,data normalized and data enhanced,and then input into two models for classification.By comparing with the existing different feature extraction and classification methods,the experimental results show that the deep convolutional network model lchieves better classification results in the field of multi-class motor imaging EEG decoding research.Keywords:brain-computer interface,motor imagination,deep convolutional network,EEG classification脑-(计算机)机接口(Brain-computar Intarfaca,BCI)可以简单定义为提供大脑控制外部设备以实现通信或控制的一个系统[1]O BCI有显著的潜力可以帮人们替代或恢复由疾病或伤害而受损的功能。
人体膝关节的力矩参数
人体膝关节的力矩参数赵宏垚;徐秀林【期刊名称】《中国组织工程研究》【年(卷),期】2011(015)004【摘要】BACKGROUND: Parameters of knee joint are not directly measured due to complex body motion. Therefore, the parameters are obtained by simulation or reverse dynamics.OBJECTIVE: To conclude and compare the method of determining torque parameters, and to find a reasonable, responsible,practical method.METHODS: A computer-based online search of CNKI, Wanfang, and Science Direct databases, articles related to measurements of torque parameters were searched with key words "knee joint, torque, isokinetic dynamometer, lagrangian modeling, neural network modeling" in Chinese and English. Correlated and newly-published articles were included.RESULTS AND CONCLUSION : A total of 123 articles were collected, and 75 were firstly selected following exclusion of repetitive articles. Finally, 35 articles were included according to title and abstract. Results show that isokinetic dynamometer has been extensively used, but reliability is low, and the price of dynamometer is expensive. Lagrangian modeling and neural network modeling can be modified to obtain reliability and practicality.%背景:由于人体运动过程复杂,难以用直接的方法来测量膝关节各项力学参数,所以,通常使用模拟或根据逆向动力学推算的方式来求得所需参数值.目的:总结和比较膝关节力矩参数的测定方法,找出一种合理、可靠性高、实用性广的方法.方法:应用计算机检索CNKI,万方数据库,Elsevier公司的Science Direct电子期刊关于膝关节力矩参数测试方法的文献,以"膝关节,力矩,等速测力法,拉格朗日建模,神经网络建模"及"kneejoint,torque,isokinetic dynamometer,lagrangian modeling,neural network modeling"为检索词进行检索.纳入文章内容与人体膝关节力矩参数相关,同一领域文献则选择近期发表或发表在权威杂志文章.结果与结论:检索文献量总计123篇,排除重复性研究,初检得到75篇文献,阅读标题和摘要进行初筛,共保留其中的35篇归纳总结.结果表明,等速测力虽是主流方法,但是存在可靠性不够高的问题,且相应的等速测力仪价格昂贵,而朗格朗日建模与神经网络建模可以通过改变各自的模型来达到期望的要求,故有更大的可靠性与实用性,后者有更大的发展前景.【总页数】4页(P705-708)【作者】赵宏垚;徐秀林【作者单位】上海理工大学医疗器械与食品学院,上海市杨浦区,200090;上海理工大学医疗器械与食品学院,上海市杨浦区,200090【正文语种】中文【中图分类】R318【相关文献】1.等速向心/离心训练对膝关节峰力矩和屈/伸肌峰力矩比影响的比较 [J], 柳华;杨翼;黄晓琳2."腘绳肌离心收缩力矩/股四头肌向心收缩力矩"在预防腘绳肌运动性拉伤和膝关节前交叉韧带损伤方面的应用 [J], 曹峰锐3.人体下肢鞭打髋、膝关节力矩变化特征研究 [J], 唐丽萍;李世明;姜丽;韩立明4.肌电与肌力矩、膝关节角度、膝关节运动速度之关系(摘要) [J], 蒋海鹰;张汇兰;林建英;龚明5.中国人体惯性参数研究之三－女性人体（18～23岁）体段参数的CT法测定 [J], 石岫昆;董亚凡;姚树人;刘磊因版权原因，仅展示原文概要，查看原文内容请购买。