基于动态不确定因果图的推理算法研究
合集下载
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
Chongqing Normal University
May，2020
重庆师范大学硕士学位论文
中文摘要
基于动态不确定因果图的推理算法研究
摘要
在现代人工智能技术领域，知识表达和基于该知识表达的推理算法在构造智 能系统的过程中至关重要。现代智能系统需处理的知识类信息大部分为不确定因 果关系类信息，因此有必要对处理不确定因果关系类信息的智能系统作进一步的 研究。动态不确定因果图源于贝叶斯网，是一种在符合概率论基本规律的前提下， 用于处理实际问题中不确定因果关系类信息的方法。简洁的知识表达能力和合理 有效的推理方法使得动态不确定因果图在故障诊断、风险评估、预测等诸多领域 的广泛应用越来越受到国际上的认可。
1.3.1 论文创新点.................................................................................................. 4 1.3.2 论文结构 ..................................................................................................... 4
II
重庆师范大学硕士学位论文
英文摘要
involving complex directed cycles. The algorithm searches and breaks the loops according to the prescribed path rules. The results of the examples further prove the rationality and reliability of the algorithm.
硕士学位论文 基于动态不确定因果图的推理算法研究
胡婷婷
指 导 教 师：王洪春 教授 专 业 名 称：概率论与数理统计 研 究 方 向：应用数理统计
二 O 二 O 年五月
重庆师范大学硕士学位论文
基于动态不确定因果图的推理算法研究
硕士研究生：胡婷婷 指导教师：王洪春 教 授 学科专业：概率论与数理统计 所在学院：数学科学学院
重庆师范大学
二 O 二 O 年五月
A Thesis Submitted to Chongqing Normal University in Partial Fulfillment of the Requirements for the Degree of
Master
Research on Reasoning Algorithm Based on Dynamic Uncertain Causality Graph
(1) Aiming at the construction of dynamic uncertain causality graph model, in this paper, the traditional construction method is given at first, secondly, for the existing typical fault tree, three steps of DUCG model transition are summarized by using the idea of taking part as a whole, which are event transition, numerical transition and logical transition, and then the transition algorithm is proposed to realize the transition to a more advantageous model.
1.1 研究背景........................................................................................................... 1 1.2 研究现状........................................................................................................... 2 1.3 创新点及论文结构 ............................................................................................. 4
This paper focuses on the knowledge representation and reasoning algorithm of dynamic uncertain causality graph (DUCG). The main contents list as follows:
(3) For the directed cyclic graphs in DUCG model, two classes of cyclic graphs are defined, and the general forms are induced according to the traditional reasoning process of single-valued and multi-valued DUCG, respectively. A new reasoning algorithm based on causal intensity matrix is proposed to solve the DUCG model
本论文重点研究了动态不确定因果图（Dynamic Uncertain Causality Graph, DUCG）的知识表示和推理算法，主要内容如下：
（1）针对动态不确定因果图理论模型构建这类问题，本文首先给出了传统的 构建方法，其次对于已存在的典型故障树，利用化局部为整体的思想，提出了向 DUCG 模型转化的三个步骤，分别是事件转化、数值转化和逻辑转化，进一步提 出了转化算法，以更好实现向更具优点的模型转化。
（2）针对 DUCG 模型中条件作用事件的复杂性，首先对单值 DUCG 中的条 件连接事件和多值 DUCG 中的条件作用事件分别作了详细的讨论，其次依据条件 作用事件的类型提出了两个实用性强的定理，实例验证与传统推理算法相比，所 给定理更适用于复杂条件情形下变量逻辑表达式的展开。
（3）对于 DUCG 模型中含有有向循环图这类问题，本文首先定义了两类循 环图，其次依据单值 DUCG 和多值 DUCG 中的传统解环推理过程分别归纳了适 用的通用形式，用于解决涉及简单有向循环的 DUCG 模型；针对含有复杂有向循 环的 DUCG，本文提出一种基于因果强度矩阵新的推理算法，按照规定的路径规 则进行搜索解环，所举实例更进一步证明了该算法的合理性和可靠性。
In the field of modern artificial intelligence technology, knowledge representation and reasoning algorithm based on it are very important in the process of constructing intelligent system. The knowledge information that modern intelligent system needs to process is mostly uncertain causal information, so it’s necessary to study the intelligent system that can process uncertain causal information easily. Dynamic uncertain causality graph is a method to deal with uncertain causal information in practical problems on the premise of conforming to the basic laws of probability theory. The concise knowledge representation and reasonable and effective reasoning methods make the wide application of dynamic uncertain causality graph in fault diagnosis, risk assessment and prediction, which is more and more accepted by the world.
Key words: DUCG, Fault tree, Conditional event, Directed cyclic graph, Causal intensity ma位论文
目录
目录
中文摘要................................................................................................................. I 英文摘要................................................................................................................ II 1 绪论 .................................................................................................................... 1
Candidate: Tingting Hu Supervisor: Hongchun Wang Professor Major: Probability Theory and Mathematical Statistics College: School of Mathematical Sciences
(2) In view of the complexity of conditional events in DUCG model, conditional concatenation events in single-valued DUCG and conditional events in multi-valued DUCG are discussed in detail at first, then two practical theorems are proposed according to the type of conditional event, the results show that the given theorems are more suitable for the expansion of variable logic expression under complex conditions than traditional reasoning algorithm.
关键词：DUCG，故障树，条件作用事件，有向循环图，因果强度矩阵
I
重庆师范大学硕士学位论文
英文摘要
Research on Fault Diagnosis of Causality Diagram Based on Binary Decision Graph and Fuzzy Inference
ABSTRACT
May，2020
重庆师范大学硕士学位论文
中文摘要
基于动态不确定因果图的推理算法研究
摘要
在现代人工智能技术领域，知识表达和基于该知识表达的推理算法在构造智 能系统的过程中至关重要。现代智能系统需处理的知识类信息大部分为不确定因 果关系类信息，因此有必要对处理不确定因果关系类信息的智能系统作进一步的 研究。动态不确定因果图源于贝叶斯网，是一种在符合概率论基本规律的前提下， 用于处理实际问题中不确定因果关系类信息的方法。简洁的知识表达能力和合理 有效的推理方法使得动态不确定因果图在故障诊断、风险评估、预测等诸多领域 的广泛应用越来越受到国际上的认可。
1.3.1 论文创新点.................................................................................................. 4 1.3.2 论文结构 ..................................................................................................... 4
II
重庆师范大学硕士学位论文
英文摘要
involving complex directed cycles. The algorithm searches and breaks the loops according to the prescribed path rules. The results of the examples further prove the rationality and reliability of the algorithm.
硕士学位论文 基于动态不确定因果图的推理算法研究
胡婷婷
指 导 教 师：王洪春 教授 专 业 名 称：概率论与数理统计 研 究 方 向：应用数理统计
二 O 二 O 年五月
重庆师范大学硕士学位论文
基于动态不确定因果图的推理算法研究
硕士研究生：胡婷婷 指导教师：王洪春 教 授 学科专业：概率论与数理统计 所在学院：数学科学学院
重庆师范大学
二 O 二 O 年五月
A Thesis Submitted to Chongqing Normal University in Partial Fulfillment of the Requirements for the Degree of
Master
Research on Reasoning Algorithm Based on Dynamic Uncertain Causality Graph
(1) Aiming at the construction of dynamic uncertain causality graph model, in this paper, the traditional construction method is given at first, secondly, for the existing typical fault tree, three steps of DUCG model transition are summarized by using the idea of taking part as a whole, which are event transition, numerical transition and logical transition, and then the transition algorithm is proposed to realize the transition to a more advantageous model.
1.1 研究背景........................................................................................................... 1 1.2 研究现状........................................................................................................... 2 1.3 创新点及论文结构 ............................................................................................. 4
This paper focuses on the knowledge representation and reasoning algorithm of dynamic uncertain causality graph (DUCG). The main contents list as follows:
(3) For the directed cyclic graphs in DUCG model, two classes of cyclic graphs are defined, and the general forms are induced according to the traditional reasoning process of single-valued and multi-valued DUCG, respectively. A new reasoning algorithm based on causal intensity matrix is proposed to solve the DUCG model
本论文重点研究了动态不确定因果图（Dynamic Uncertain Causality Graph, DUCG）的知识表示和推理算法，主要内容如下：
（1）针对动态不确定因果图理论模型构建这类问题，本文首先给出了传统的 构建方法，其次对于已存在的典型故障树，利用化局部为整体的思想，提出了向 DUCG 模型转化的三个步骤，分别是事件转化、数值转化和逻辑转化，进一步提 出了转化算法，以更好实现向更具优点的模型转化。
（2）针对 DUCG 模型中条件作用事件的复杂性，首先对单值 DUCG 中的条 件连接事件和多值 DUCG 中的条件作用事件分别作了详细的讨论，其次依据条件 作用事件的类型提出了两个实用性强的定理，实例验证与传统推理算法相比，所 给定理更适用于复杂条件情形下变量逻辑表达式的展开。
（3）对于 DUCG 模型中含有有向循环图这类问题，本文首先定义了两类循 环图，其次依据单值 DUCG 和多值 DUCG 中的传统解环推理过程分别归纳了适 用的通用形式，用于解决涉及简单有向循环的 DUCG 模型；针对含有复杂有向循 环的 DUCG，本文提出一种基于因果强度矩阵新的推理算法，按照规定的路径规 则进行搜索解环，所举实例更进一步证明了该算法的合理性和可靠性。
In the field of modern artificial intelligence technology, knowledge representation and reasoning algorithm based on it are very important in the process of constructing intelligent system. The knowledge information that modern intelligent system needs to process is mostly uncertain causal information, so it’s necessary to study the intelligent system that can process uncertain causal information easily. Dynamic uncertain causality graph is a method to deal with uncertain causal information in practical problems on the premise of conforming to the basic laws of probability theory. The concise knowledge representation and reasonable and effective reasoning methods make the wide application of dynamic uncertain causality graph in fault diagnosis, risk assessment and prediction, which is more and more accepted by the world.
Key words: DUCG, Fault tree, Conditional event, Directed cyclic graph, Causal intensity ma位论文
目录
目录
中文摘要................................................................................................................. I 英文摘要................................................................................................................ II 1 绪论 .................................................................................................................... 1
Candidate: Tingting Hu Supervisor: Hongchun Wang Professor Major: Probability Theory and Mathematical Statistics College: School of Mathematical Sciences
(2) In view of the complexity of conditional events in DUCG model, conditional concatenation events in single-valued DUCG and conditional events in multi-valued DUCG are discussed in detail at first, then two practical theorems are proposed according to the type of conditional event, the results show that the given theorems are more suitable for the expansion of variable logic expression under complex conditions than traditional reasoning algorithm.
关键词：DUCG，故障树，条件作用事件，有向循环图，因果强度矩阵
I
重庆师范大学硕士学位论文
英文摘要
Research on Fault Diagnosis of Causality Diagram Based on Binary Decision Graph and Fuzzy Inference
ABSTRACT