一种改进的Elman神经网络算法

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基于EMD和改进PSO-Elman神经网络的液压故障诊断

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第２２卷
第４期
计算机技术与发展
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２１０２年４月
Ｖ０．２Ｎｏ４１２．Ａｐｒ．２２０１
基于Ｅ改进ＰＯ— ｌｎ神经ＭＤ和ＳＥｍａ网络的液压故障诊断
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基于蚁群优化的Elman神经网络在故障诊断中的应用研究

第２７卷第４期总９４期
基于蚁群优化的Ｅｌｍａｎ神经网络在故障诊断中
的应用研究
曹龙汉，牟浩，张迁，余佳玲，李景南
４０００３５）（重庆通信学院控制工程重点实验室，重庆
［摘要］指出普通Ｅｌｍａｎ神经网络ＢＰ学习算法的不足，将蚁群算法和Ｅｌｍａｎ神经网络相融
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保密风险指数评估与预测模型及应用

科学技术创新2020.291概述军工单位的保密风险管理主要集中在建立适用于本单位保密风险管理体系，梳理单位各个作业面的风险点并进行风险评估，制定相应的风险控制措施，实现优化保密业务管理，以最小成本将失泄密风险控制在单位业务工作可以接受的水平，以此获得最大的信息安全防护。

全面进行保密系统的风险评估的首要关键问题建立健全的指标体系并量化计算。

当评估指标数量不太多的时候，传统的数学模型能够在定量分析上取得较合理的评估结果，例如采用定性和定量相结合的保密管理风险评估方法，计算出年度保密风险的分布状态[1];采用归一化、标准化及层次分析法等方法对模型指标进行求解，建立了保密工作风险评估模型[2]。

但是当指标数量较多时，由于模型本身的局限性,难以处理随机干扰因素对风险评估数据的影响，因而很难正确反映风险评估数据或者风险指数本身的高度不确定性与非线性，导致在风险评估及风险指数预测方面产生较大的误差。

因此，本文从单位保密实际工作出发，对定期获得的保密风险指数，采用K-means 聚类分析方法，识别出保密风险评估指标体系高风险指标集，将识别出来的高风险指标作为当前保密工作的防控重点；同时为了加强保密工作的风险预警，结合风险指数的历史评估数据，采用Elman 神经网络，预测各个指标在下一个时间段的风险指数值，并对预测为高风险的指标或者风险点采取主动预警防范措施，提高单位保密风险管理水平。

2保密风险指数及K-Means 聚类评估2.1风险指数及风险等级结合保密风险发生可能性大小、影响程度和管理改进迫切性三个维度[3]，根据数学模型可以计算出单位的保密风险指数，并确定该单位的风险等级[4]。

表1风险水平等级确定与排序列表为了便于问题描述，本文假设风险评估指标数量为n ，对于每个指标统计了第个月的风险指数值。

2.2基于K-means 聚类的高危指标筛选K-means 聚类算法基本思想是:以空间中k 个点为形心进行聚类，以数据点到形心的欧式距离作为优化函数，利用函数求极值的方法得到迭代运算的调整规则[5]。

基于改进型Elman神经网络预测模糊神经网络控制的变风量空调设计

房问的灯光负荷、设备负荷差别较大，每层采用１台组合式空调机，造成了房间温度因不能调节
而产生的过冷或过热的现象，同时各设备问及演
播室、语录室冬季严重超温。
效果。由于变风量空调系统灵活性高，节能性强，因此，已成为现代智能建筑领域炙手可热的研究对象。现在大部分空调控制仍采用传统的
中图分类弩：ＴＰ３９１文献标志码：Ｂ文誊缡号：１６７４～８４ｌ７（２（）１３）０１４）０５３４）５
０引言
变风量（ＶａｒｉａｂｌｅＡｉｒＶｏｌｕｍｅ，ＶＡＶ）空调系统是一种全空气系统，它通过使送风温度固定，并自动根据室内参数要求的变化或空调的负荷变化情况调节送风量，因此获得了显著的节能
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个关键步骤是确定隐层结点数ｆ，其中ｆ的初始值
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变风量空调系统主要由空气处理机组、送回风系统（送、回风风道）、末端装置（有些末端装置带有送风散流器）和相关的自动控制系统组成。
１工程背景

基于改进WOA-Elman神经网络的高光谱牛奶蛋白质快速无损检测

差为０．０３３０.肖仕杰等[７]为实现牛奶蛋白质的无损快速分级,基于红外光谱信息提出了将信息变量去除法㊁自适应加权采样算法和随机森林相结合用于牛奶蛋白质检测.其检测准确率达９６．４８％,检测时间为５．３３s,可实现牛奶蛋白质的快速无损检测.王丰霞等[８]将凯氏定氮法用于牛奶蛋白质检测,对不确定度产生的原理进行了分析,检测结果为(３．６２ʃ０．０２)g/１００g.上述方法虽然可以实现牛奶蛋白质含量的检测,但在高光谱检测中,牛奶成分复杂,变量相关性较低,所以检测精度和效率较差,有待进一步提高.研究拟提出一种将高光谱技术与机器学习方法相结合的牛奶蛋白质含量快速无损检测方法,通过高光谱成像技术采集牛奶表面的高光谱图像,采用改进鲸鱼算法(w h a l e o p t i m i z a t i o n a l g o r i t h m,WO A)与E l m a n神经网络结合实现牛奶蛋白质含量的快速无损检测,旨在为牛奶品质检测提供一种快速㊁无损的检测方法.１㊀高光谱成像系统概述高光谱成像系统(图１)主要由载物台㊁检测样品㊁光源㊁镜头㊁光谱仪㊁相机传感器和计算机等组成,采集系统运行在封闭暗箱中,以降低外部环境光对其造成影响,但通过相机采集会产生一定的噪音.因此,为了降低噪声等干扰,需对采集图像进行预处理,试验通过多元散射校正(m u l t i p l i c a t i v e s c a t t e r c o r r e c t i o n,M S C)算法对高光谱数据进行预处理[９－１０].图高光谱成像系统F i g u r e１㊀H y p e r s p e c t r a l i m a g e r２㊀牛奶蛋白质含量检测模型２．１㊀数据降维方法采用高光谱仪的光谱范围为４００~１０００n m,牛奶高光谱图波段为１２５个,通过全波长建立的模型存在复杂和训练时间长等问题.因此,需对高光谱数据进行降维,适当的降维方法不仅可以加快处理速度,还会提高算法精度,对处理高维数据具有重要的意义.试验选择竞争性自适应重加权采样C A R S算法对牛奶高光谱数据进行降维处理,利用自适应重加权采样算法保留权值较大的集合,建立偏最小二乘回归模型,引入交叉验证,不断优化计算均方根误差R M S E,R M S E最小子集即精度最高的特征波长组合[１１－１３].２．２㊀E l m a n神经网络E l m a n网络是一种递归神经网络,不仅计算能力强,还可以增强网络的全局稳定性,适用于大规模数据的预测问题[１４－１６].试验利用E l m a n神经网络实现牛奶蛋白质含量的快速无损检测,相比于B P神经网络,E l m a n神经网络(图)加入了承接层.图网络结构F i g u r e２㊀N e t w o r ks t r u c t u r e㊀㊀通过承接层记录隐含层上一节点的输出,从而实现历史数据的存储功能,使网络具有适应时变的能力.相比于前馈型神经网络,E l m a n神经网络的计算能力更强,稳定性也更强.y(t)＝g(ω３x(t)),(１) x(t)＝f(ω１x c(t)＋ω２(u(t－１))),(２) x c(t)＝x(t－１),(３)式中:ω１㊁ω２㊁ω３层间权值;g(x) 输出层传递函数;f(x) 隐含层传递函数;x(t) 隐含层输出;y(t) 输出向量;u(t) 输入向量;x c(t) 承接层输出向量.但传统的E l m a n神网络预测精度受初始权值和阈值的影响较大,容易陷入局部最优,而WO A算法具有结构简单㊁搜索能力强㊁收敛速度快等特点.２．３㊀改进鲸鱼算法WO A算法是一种新型的元启发式优化算法,主要由围捕猎物㊁泡网捕食和搜索猎物３部分组成[１７－２０].(１)围捕猎物:鲸鱼在狩猎时包围猎物,通过包围猎物更新自身的位置,如式(４)㊁式(５)所示.D＝C x d p(t)－x d i(t),(４) x d i(t＋１)＝x d p(t)－A D,(５)６５安全与检测S A F E T Y&I N S P E C T I O N总第２６６期|２０２３年１２月|式中:A ㊁C 系数向量;x di (t )㊁x d p (t ) 第i 头鲸鱼位置和当前最优位置; 逐个元素相乘;t迭代次数.A 和C 由式(６)㊁式(７)计算.A ＝２a r －a ,(６)C ＝２ r ,(７)式中:a 算法的收敛因子;r [０,１]的随机数.(２)泡网捕食:鲸鱼在用螺旋气泡包围猎物的同时,还要不断缩小包围网,螺旋位置更新为x d i (t ＋１)＝D ∗ e b lc o s (２πl )＋xd p (t ),(８)式中:D ∗当前个体到最佳个体的距离(迭代t 次);b常数;l [－１,１]的随机数.鲸鱼通过随机包围和螺旋气泡法更新位置,根据随机数p 进行分割,如式(９)所示.x di (t ＋１)＝x dp(t )－A D ,p ＜０．５D ∗e b lc o s (２πl )＋xd p (t ),p ȡ０．５{.(９)(３)搜索猎物:搜索猎物是为了找到更好的解,如果|A |ȡ１,鲸鱼在圈外随机进行搜索,如式(１０)所示.D ＝C X r a n d －X ,(１０)式中:X r a n d随机选取鲸群中的任意个体位置.但WO A 算法仍存在收敛速度慢㊁易陷入局部最优等问题.试验采用混沌映射㊁收敛因子优化和权重优化对WO A 算法进行优化,提高搜索精度,跳出局部极值.(１)混沌映射:通过混沌映射对种群进行初始化,提高WO A 算法搜索精度.使用s i n e 映射初始化种群,其计算式为z (k ＋１)＝s４s i n (πz (k )),(１１)式中:s [０,４]的系数;z (k ) k 次迭代后的结果.(２)自适应收敛因子:在现有鲸鱼优化算法中,收敛因子a 对全局和局部搜索性能有较大影响.在WO A 算法中,收敛因子a 为２~０,其前期全局搜索能力较差,后期局部搜索能力较差.试验采用分段非线性调整策略对其进行优化:a ＝T ２m a x ４ˑ((t －T m a x ２)２＋１),t ɤT m a x ２T ２m a x ４ˑ(t －T m a x )２,t ＞T m a x ２ìîíïïïï,(１２)式中:T m a x最大迭代次数.(３)自适应权重:为了避免WO A 算法后期早熟收敛,试验对权重ω进行自适应调节,在前期加强全局搜索,避免陷入局部最优,后期增强鲸鱼位置更新速度,如式(１３)所示.ω＝r a n d ˑc o s (π２ˑ(１－tT m a x)).(１３)２．４㊀检测模型对于E l m a n 神经网络来说,权值和阈值是其需要优化的参数,试验采用改进的WO A 算法对E l m a n 神经网络参数进行优化,使模型精度更高,收敛速度更快.模型检测流程:步骤１:输入数据预处理,试验选择C A R S 算法对牛奶高光谱数据进行降维处理.步骤２:对E l m a n 神经网络参数进行初始化,包括输入输出和隐含层节点数等.步骤３:对改进WO A 算法进行初始化,包括鲸鱼种群㊁最大迭代数等.步骤４:对最优位置和最优适应度值进行计算.WO A 算法的适应度函数采用均方根误差R M S E :R M S E ＝１NðNi ＝１(y i－y ᶄi )２,(１４)式中:y i ㊁yᶄi 第i 个数据实际值和模型输出值.步骤５:使用改进的WO A 算法迭代优化E l m a n 神经网络的权值(ω１㊁ω２㊁ω３)和阈值(b １㊁b ２),当满足结束条件时结束循环,输出最优值,即E l m a n 神经网络的权值和阈值.否则,返回步骤３.步骤６:通过得到的权重和阈值训练E l m a n 神经网络,并对测试集牛奶蛋白质含量进行检测.３㊀模型试验３．１㊀试验参数为了验证试验方法的性能,P C 选择华为笔记本,操作系统为W i n d o w s １１６４位旗舰,I n t e l i ５１３４００C P U ,频率４．０G H z ,１６G B 内存.试验使用的高光谱仪P T U ＧD ４８E采集各样品的高光谱数据,并通过训练集对所提模型的初始参数进行微调.数据源为１０个不同牛场新鲜液态奶,包括蒙牛㊁Q Q 星㊁伊利㊁光明㊁特仑苏,分别选择蛋白质含量为３．０,３．２,３．３,３．４,３．６g /１００m L 的样品各２００个,共１０００个样品作为试验样品集.将其均分为训练集㊁试验集和测试集,训练集ʒ试验集ʒ测试集＝８ʒ１ʒ１.试验参数见表１.㊀㊀不同评价指标对模型会产生不同的结果,试验选取训练集均方根误差(R M S E C )㊁测试集均方根误差(R M S E P )㊁决定系数R ２和检测时间对模型进行评价.７５|V o l ．３９,N o ．１２曹纪磊等:基于改进WO A ＧE l m a n 神经网络的高光谱牛奶蛋白质快速无损检测表１㊀试验参数T a b l e １㊀T e s t p a r a m e t e r s算法参数数值E l m a n迭代次数１０００学习率０．００１动量因子０．０１最小性能梯度１e －０６训练最小误差０．０００１隐含层节点数１４改进WO A种群３０最大迭代次数５０自适应权值[０．１,０．９]R M S E C ＝１N C －１ðN Ci ＝１(y i －y ᶄi )２,(１５)R M S E P ＝１N P －１ðN Pi ＝１(y i －y ᶄi )２,(１６)R ２＝１－ðN Pi ＝１(y i－y ᶄi )i ２ðN C i ＝１(y i － y i )２,(１７)式中:N C ㊁N P训练集和测试集数量;y i ㊁y ᶄi ㊁ yi 第i 个样本实际值㊁预测值和平均值.检测时间为模型对测试集进行检测的总时间.３．２㊀牛奶蛋白质检测试验为了验证试验所提改进WO A 算法的优化能力,与优化前的WO A 算法进行比较,对E l m a n 参数进行优化,不同方法随迭代次数变化的适应度值如图所示.图３㊀不同方法随迭代次数变化的适应度值F i g u r e ３㊀T h e f i t n e s s v a l u e s o f d i f f e r e n tm e t h o d s v a r yw i t h t h en u m b e r o f i t e r a t i o n s㊀㊀由图３可知,WO A 算法在迭代第３３次时收敛,个体最优适应度值最低为０．０１１１.改进WO A 算法在迭代第２１次时收敛,个体最优适应值最低为０．０００２,收敛精度较高.说明采用混沌映射㊁自适应收敛因子和自适应权重优化WO A 算法可以提高WO A 算法在牛奶蛋白质含量检测中的收敛精度.为了验证试验所提改进WO A ＧE l m a n 模型在牛奶蛋白质检测中的优越性,对试验方法和WO A ＧE l m a n 模型进行比较,将训练后的模型用于测试集测试,不同方法的检测结果和实际值比较如图４所示,因试验方法和WO A ＧE l m a n 模型预测值与实际值较为接近,为了便于阅读,将试验方法检测的蛋白质数据向上移动０．０５,将WO A ＧE l m a n 模型检测的蛋白质数据向上移动０．１.不同方法的性能指标见表.图不同方法检测结果与实际值对比F i g u r e ４㊀C o m pa r i s o no f d e t e c t i o n r e s u l t sw i t ha c t u a l v a l u e su s i n g di f f e r e n tm e t h o d s 表２㊀不同方法性能指标T a b l e ２㊀P e r f o r m a n c e i n d i c a t o r s o f d i f f e r e n tm e t h o d s检测方法数据集均方根误差决定系数试验方法训练集０．０００２０．９９９６测试集０．０００３０．９９７３WO A ＧE l m a n 模型训练集０．０１１１０．９８２５测试集０．０２８００．９６７５㊀㊀由图４可知,试验方法和WO A ＧE l m a n 神经网络模型的检测效果均较好,但试验方法的检测结果最接近牛奶蛋白质含量的真实值,试验方法对牛奶蛋白质含量的检测误差最小.由表２可知,试验方法的精度最高,测试集的决定系数达０．９９７３,均方根误差为０．０００３.与WO A ＧE l m a n 神经网络模型相比,试验方法的决定系数提高了３．０８％,均方根误差降低了９８．９３％.因此,试验所提改进WO A 算法可以实现E l m a n 神经网络模型权值和阈值的优化,有效提高了模型的拟合能力和检测精度,在牛奶蛋白质含量检测中具有较好的效果.为了进一步分析试验所提模型的优势,将试验方法与C A R S ＧS S A ＧS VM 模型[５]㊁C A R S ＧS P A ＧB P 模型[６]和C A R S ＧP S O ＧS VM 模型[２１]进行对比,结果如表３所示.表３㊀不同方法性能指标结果对比T a b l e ３㊀C o m pa r i s o no f p e r f o r m a n c e i n d i c a t o r s o f d i f f e r e n tm e t h o d s检测方法均方根误差决定系数检测时间/s 试验方法０．０００３０．９９７３１．５６C A R S ＧS S A ＧS VM 模型０．００１１０．９８２５１．７５C A R S ＧS P A ＧB P 模型０．０９０３０．９２８７６．５８C A R S ＧP S O ＧS VM 模型０．０３１６０．９６４９４．１２８５安全与检测S A F E T Y &I N S P E C T I O N 总第２６６期|２０２３年１２月|㊀㊀由表３可知,试验方法具有最高的检测精度和较低的检测时间,其均方根误差为０．０００３,决定系数为０．９９７３,检测时间为１．５６s.与C A R SＧS S AＧS VM模型㊁C A R SＧS P AＧB P模型和C A R SＧP S OＧS VM模型相比,试验方法的均方根误差分别下降了７２．７２％,９９．６６％,９９．０５％,决定系数分别上升了１．５１％,７．３９％,３．３６％,检测时间分别下降了１０．８７％,６１．０９％,６２．１４％.这是因为试验方法通过改进WO A算法优化E l m a n神经网络模型提高了检测性能,在牛奶蛋白质检测中表现的性能最好.４㊀结论研究基于高光谱成像系统,提出了一种结合改进鲸鱼算法和E l m a n神经网络的牛奶蛋白质含量快速无损检测方法.通过优化改进鲸鱼算法,提高搜索精度,采用改进鲸鱼算法优化E l m a n神经网络的权重和阈值,提高检测性能.结果表明,试验方法在牛奶蛋白质检测中具有较好的精度和较低的检测时间.与C A R SＧS S AＧS VM模型㊁C A R SＧS P AＧB P模型和C A R SＧP S OＧS VM模型相比,试验方法的均方根误差下降了８０％左右,决定系数上升了３％左右,检测时间下降了３０％左右,具有一定的优势.但也存在一些可以提升的空间,如仅对新鲜牛奶进行检测,无法在食品无损检测中大范围地推广使用,后期将不断优化和完善模型的性能,尽快在食品无损检测中大范围推广.参考文献[1]白丽萍,王伟,王强,等.近红外光谱快速检测葡萄酒品质[J].浙江农业科学,2021,62(2):389Ｇ391,400.BAI L P,WANG W,WANG Q,et al.Rapid detection of wine quality by nearＧinfrared spectroscopy[J].Agricultural Science,2021, 62(2):389Ｇ391,400.[2]项辉宇,薛真,冷崇杰,等.基于Halcon的苹果品质视觉检测试验研究[J].食品与机械,2016,32(10):123Ｇ126.XIANG H Y,XUE Z,LENG C J,et al.Experimental study on visual inspection of apple quality based on Halcon[J].Food& Machinery,2016,32(10):123Ｇ126.[3]朱晓琳.基于高光谱成像的水果品质及木材含水量评估方法[D].无锡:江南大学,2020:7Ｇ8.ZHU X L.Method for evaluating 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Technology,2021,38(5):388Ｇ394.(下转第１１６页)９５|V o l．３９,N o．１２曹纪磊等:基于改进WO AＧE l m a n神经网络的高光谱牛奶蛋白质快速无损检测基金项目:国家社科基金重大项目(编号:１４Z D C ０２２)作者简介:崔格豪(１９８５ ),男,大连海事大学在读博士研究生.E Ｇm a i l :c h a r l e s h a o ＠１６３．c o m收稿日期:２０２２Ｇ０９Ｇ０６㊀㊀改回日期:２０２３Ｇ１１Ｇ２０D O I :１０．１３６５２/j ．s p j x ．１００３．５７８８．２０２２．８０７７１[文章编号]１００３Ｇ５７８８(２０２３)１２Ｇ００６０Ｇ０５食品市场职业打假人权利的法律边界L e g a l b o u n d a r y o f t h e r i g h t s o f pr o f e s s i o n a l c o u n t e r f e i t e r s i n f o o dm a r k e t 崔格豪C U IG e h a o(大连海事大学法学院,辽宁大连㊀１１６０２３)(D a l i a n M a r i t i m eU n i v e r s i t y L a wS c h o o l ,D a l i a n ,L i a o n i n g １１６０２３,C h i n a )摘要:聚焦食品市场,从职业打假案件的诉讼现状入手,总结分析了目前理论与实务中关于职业打假行为存在的问题与争议,讨论了职业打假人知假买假的权利保护限制必要性,并尝试从多个层面对其职业打假行为进行引导规制,如明确职业打假人的法律地位㊁引导打假行为正当化规范化等.关键词:职业打假人;惩罚性赔偿;预包装食品标签;法律边界A b s t r a c t :F o c u s i n g o n t h e f o o d m a r k e t ,s t a r t i n g fr o m t h e l i t i g a t i o ns t a t u s o f p r o f e s s i o n a la n t i Ｇc o u n t e r f e i t i n g c a s e s ,t h i s p a p e r s u mm a r i z e s a n da n a l y z e s t h e p r o b l e m sa n dd i s p u t e sa b o u t p r o f e s s i o n a la n t i Ｇc o u n t e r f e i t i n g b e h a v i o ri n c u r r e n tt h e o r y a n d p r a c t i c e ,d i s c u s s e s t h en e c e s s i t y o f p r o t e c t i n g a n dr e s t r i c t i n g th e r i g h to f p r o f e s s i o n a la n t i Ｇc o u n t e r f e i t e r st o k n o w a n d b u y f a k e g o o d s ,a n dt r i e st o g u i d ea n dr e gu l a t et h e i r p r o f e s s i o n a la n t i Ｇc o u n t e r f e i t i n g b e h a v i o rf r o m m u l t i p l el e v e l s ,s u c ha sc l a r i f y i n gt h e l e g a l s t a t u s o f p r o f e s s i o n a l a n t i Ｇc o u n t e r f e i t e r s a n d g u i d i n g t h e l e g i t i m a t e a n d s t a n d a r d i z e d a n t i Ｇc o u n t e r f e i t i n g b e h a v i o r ．K e yw o r d s :p r o f e s s i o n a l f a k e k i l l e r ;p u n i t i v e d a m a g e s ;p r e Ｇp a c k a g e d f o o d l a b e l s ;l e g a l b o u n d a r y１㊀职业打假人的概念界定现阶段中国并无相关法律法规对职业打假人进行明确定义.维基百科中的职业打假人则是指以打假为职业,长期寻找在产品质量㊁包装标志㊁有效期限㊁广告宣传等方面存在问题的商品,进而通过索赔或其他方式获取一定利益的群体.该定义明确了职业打假人正是因其具有的牟利性目的而区别于普通消费者.２０１９年４月,浙江省市场监督管理局发布«关于有效应对职业投诉举报行为营造良好营商环境的指导意见(征求意见稿)»,对此种职业索赔举报给出了明确定义.职业投诉举报是指:以牟利为目的,通过知假买假,甚至掉包㊁夹带㊁造假等非正常消费方式人为制造索赔理由,或者明知经营行为轻微违法,向市场监管部门投诉举报,不达目的就滥用信息公开㊁行政复议㊁行政诉讼㊁监察投诉等权利,胁迫或变相胁迫生产经营者让步,以期得到高额不当利益的行为.结合上述两种定义,文章聚焦的职业打假人即以牟利为目的,通过打假行为向生产经营者谋取高额赔偿的群体.２㊀催生食品市场中职业打假人的背景及其现状２．１㊀食品打假行为职业化的法律背景２００９年颁布的«食品安全法»第９８条对支付给消费者的惩罚性赔偿数额进行了首次规定,为价款的１０倍.２０１５年㊁２０１８年以及２０２１年修正的«食品安全法»均对这一惩罚性赔偿制度作了进一步详细的规定,明确了最低赔偿数额.此外,２０１３年修正的«中华人民共和国消费者权益保护法»(以下简称«消费者权益保护法»)３倍的赔偿标准也对此进行了规定.在国家鼓励消费者积极维权的背景下, 职业打假人应运而生.以王海为代表的一批职业打假人从出现至今,也已走过了２５年,这２５年来他们通过要求生产者㊁销售者支付１０倍惩罚性赔偿等方式净化市场,为食品行业健康发展贡献了自己的力量,但其中也不乏有不同的声音.通过打假行为可轻易获得的大额赔偿也使得打假行为职业化.食品行业中不乏小作坊生产模式的生产者,他们在产品包装销售的程序上不够完善,法律意识也相对薄弱.夸大宣传或标签标识瑕疵是否应当承担１０倍赔偿,这是否又属于不符合食品安全标准范畴,这些都可能是某些商家被打假人打假的问题[１].对一部分打假人而言,通过对这些商家打假往往能很轻易地获得赔偿,依此牟利从而职业化.０６F O O D &MA C H I N E R Y 第３９卷第１２期总第２６６期|２０２３年１２月|基金项目:江西省重点研发计划项目(编号:２０２０３B B F L ６３０６２);财政部和农业农村部国家现代农业产业技术体系(编号:C A R S Ｇ４５);国家自然科学基金项目(编号:３２０６０５５７,３２２６０６０４)作者简介:袁丽萍,女,江西师范大学在读硕士研究生.通信作者:李金林(１９８３ ),男,江西师范大学教授,博士.E Ｇm a i l :l i ji n l i n ４０５＠１２６．c o m 收稿日期:２０２３Ｇ０７Ｇ２５㊀㊀改回日期:２０２３Ｇ１１Ｇ２２D O I :１０．１３６５２/j ．s p j x ．１００３．５７８８．２０２３．８０７０４[文章编号]１００３Ｇ５７８８(２０２３)１２Ｇ０１１７Ｇ０９紫外减菌联合低温对草鱼肉品质及挥发性风味的影响E f f e c t o f U Vs t e r i l i z a t i o no n t h e q u a l i t y o f g r a s s c a r pm e a t d u r i n g t h ec h i l l i n g s t o r a ge 袁丽萍１,２Y U A N L i p i n g １,２㊀彭㊀斌１,２P E N GB i n １,２㊀钟比真１,２Z H O N GB i z h e n １,２㊀胡明明１,２HU M i n g m i n g１,２㊀李金林１,２L IJ i n l i n １,２(１．江西师范大学国家淡水鱼加工技术研发专业中心,江西南昌㊀３３００２２;２．江西师范大学生命科学学院,江西南昌㊀３３００２２)(１．N a t i o n a lR e s e a r c ha n dD e v e l o p m e n tC e n t e r f o rF r e s h w a t e rF i s hP r o c e s s i n g ,C o l l e g e o f L i f eS c i e n c e s ,J i a n g x iN o r m a lU n i v e r s i t y ,N a n c h a n g ,J i a n g x i ３３００２２,C h i n a ;２．S t a t eK e y L a b o r a t o r y o f Fo o dS c i e n c e a n d R e s o u r c e s ,N a n c h a n g U n i v e r s i t y ,N a n c h a n g U n i v e r s i t y ,N a n c h a n g ,J i a n gx i ３３００２２,C h i n a )摘要:目的:研究紫外减菌前处理联合低温冷藏对草鱼鱼肉质构品质及挥发性风味物质的影响,并确定最佳的紫外照射时间.方法:以草鱼为试验对象,采用３０W 紫外灯照射不同时间(０,３０,６０,９０,１５０m i n )预处理,测定鱼肉冷藏期间(０,３,６,９d )各品质参数(鱼肉表面细菌总数㊁汁液流失率㊁质构参数㊁色泽㊁气味感官)及挥发性成分变化.结果:当紫外照射时间为０~１５０m i n 时,照射时间越长,草鱼鱼肉表面减菌效果越好,汁液流失率越低,出现腐败气味越晚,但对鱼肉的质构㊁色泽及冷藏后期(６~９d )细菌总数影响较小.冷藏期间共检测到９０种挥发性风味物质,主要为醛类㊁酮类㊁醇类㊁酯类和酸类,其中辛醛㊁壬醛㊁己醛㊁２,５Ｇ辛二酮㊁１Ｇ己醇㊁１Ｇ辛烯Ｇ３Ｇ醇为紫外处理草鱼肉的主要挥发性成分,且冷藏后期酯类和酸类物质含量逐渐增多.对比控制组,紫外处理可有效延缓冷藏草鱼肉腥味的产生和香味的下降.当紫外照射时间为９０m i n 时,冷藏鱼肉表现出最佳的综合效果,随着照射时间的延长,鱼肉综合得分有所下降.结论:紫外减菌联合低温处理有利于延缓贮藏前期草鱼鱼肉质构品质劣化及风味下降,但对贮藏后期草鱼肉品质影响较小.关键词:紫外照射;草鱼;表面杀菌;保鲜;低温处理A b s t r a c t :O b je c t i v e :T h e ef f e c t o fU Vs t e r i l i z a t i o no n t h e t e x t u r e q u a l i t y a n d f l a v o rs u b s t a n c e so f t h e f r o z e ng r a s sc a r p me a tw a s s t u d i e d ,a n d t h e b e s t U V i r r a d i a t i o n t i m e w a s d e t e r m i n e d ．M e t h o d s :T h e g r a s s c a r p me a tw a s p r e t r e a t e dw i t hd if f e r e n tU V i r r a d i a t i o n t i m e s (０,３０,６０,９０,１５０m i n )a t３０W o f U V r a d i a t i o n ,t h e nt h e q u a l i t yp a r a m e t e r s (a p p a r e n t m o r p h o l og y,t o t a l n u m b e ro fb a c t e r i ao nt h es u r f a c eo f f i s h ,j u i c e l o s sr a t e ,t e x t u r e p a r a m e t e r s ,c o l o r ,o d o r a n d s e n s o r y )a n d v o l a t i l e c o m p o n e n t s o f t h ef i s h m e a td u r i n g c h i l l i n g s t o r a ge (０,３,６,９d )w e r em e a s u r e d ．R e s u l t s :I t s h o w e dt h a t t h e l o n g e r t h eU V i r r a d i a t i o n t i m e (０~１５０m i n ),t h eb e t t e r t h eb a c t e r i a r e d u c t i o ne f f e c t o n t h e s u r f a c e o f f i s h ,t h e l o w e r t h e ju i c e l o s s r a t e ,a n d t h e l a t e r t h e p u t r e f a c t i o n s m e l l a p p e a r e d ．H o w e v e r ,i t h a d l i t t l e e f f e c t o n t h e t e x t u r e ,c o l o r o f t h e f i s h ,a n d t h e t o t a l b a c t e r i a d u r i n gt h e l a t t e r o fc h i l l i n g s t o r a g e (６~９d )．I nt h ed e t e c t i o no ff l a v o r s u b s t a n c e s ,９０v o l a t i l e c o m p o n e n t s w e r e d e t e c t e d ,m a i n l y a l d e h y d e s ,k e t o n e s ,a l c o h o l s ,e s t e r s ,a n d a c i d s ．O c t a n a l ,n o n a n a l ,h e x a n a l ,２,５Ｇo c t a d i o n e ,１Ｇh e x a n o l ,a n d １Ｇo c t e n Ｇ３Ｇa l c o h o lw e r e t h em a i n v o l a t i l e c o m p o n e n t s a f t e rU Vt r e a t m e n t ．I n t h e l a t e rs t a g eo fc o l ds t o r a ge ,e s t e r sa n da c i d sw e r e i n c r e a s e d g r a d u a l l y ,w h i c h m i gh t b e r e l a t e d t o t h e f o r m a t i o n o f t h e c h a r a c t e r i s t i ca r o m a o ff i s h m e a t ．C o m p a r e d w i t ht h ec o n t r o l g r o u p,U V i r r a d i a t i o n t i m e o f ９０m i n s h o w e d t h e b e s t c o m p r e h e n s i v ee f f e c t ,a n dt h e q u a l i t y w a sd e c r e a s e d w i t ht h e e x t e n s i o n o f i r r a d i a t i o n t i m e ．C o n c l u s i o n :U V s t e r i l i z a t i o n p r e t r e a t m e n t c a n d e l a y t h e d e t e r i o r a t i o n o f q u a l i t y a n d t h e d e c l i n e o f f l a v o ro fr e f r i g e r a t e d g r a s sc a r p m e a ti nt h ee a r l y s t a geo f c h i l l i n g s t o r a g eb u th a s l i t t l ee f f e c to nt h e q u a l i t y o f g r a s sc a r p m e a t i n t h e l a t e r s t a g e o f c h i l l i n g s t o r a ge ．７１１F O O D &MA C H I N E R Y 第３９卷第１２期总第２６６期|２０２３年１２月|。

基于改进Elman神经网络的悬架试验系统

基于改进Elman神经网络的悬架试验系统宋崇智;赵又群【摘要】提出了一种能满足多层网络、多阶系统的改进型Elman网络,建立了基于改进Elman神经网络的PAC控制器.对六自由度悬架试验平台系统进行了控制研究,分析了悬架参数对轮荷利用率和相位角的影响.整车实验证明:参数匹配的悬架可以有效减小车身振动,降低悬架动挠度和轮胎动载荷.【期刊名称】《中国机械工程》【年(卷),期】2016(027)001【总页数】6页(P1-6)【关键词】悬架;六自由度;试验系统;神经网络【作者】宋崇智;赵又群【作者单位】南京航空航天大学,南京,210016;安徽工业大学,马鞍山,243002;南京航空航天大学,南京,210016【正文语种】中文【中图分类】U467.5悬架系统的参数设计与实验检测技术一直是车辆底盘设计人员研究的热点[1]。

为取得较好的悬架参数，学者们采用了不同的优化方法。

Imine等[2]采用带观测器的最优滑动模态变结构控制方法，对悬架参数进行了研究和实验；Song等[3]以车辆的乘坐舒适性、车辆对路面的损坏性和车辆平顺性指标为目标函数，采用三目标仿生蜥蜴协同进化算法对悬架参数进行了优化设计，并取得了一定成效；Wang等[4]等对蓄能悬架的结构参数进行了优化设计和实验分析，使乘坐舒适性和整车性能均得到了提升。

在实验分析及检测方面，Nieto等[5]采用测量最小轮荷利用率的方法对悬架系统进行评价。

国内学者常采用冲击载荷法来检测评定悬架系统，通过与悬架系统初始参数的比较来评价悬架的性能。

但目前的研究基本上把整车平顺性或操纵稳定性作为目标函数，通过简化整车模型，以性能参数为约束条件来进行求解分析，求解结果存在缺陷，无法得到完整的系统最优解，甚至无法进行实验验证。

笔者在分析车辆悬架动力学和悬架性能评价指标的基础上，设计了六自由度悬架试验平台系统以及改进的Elman网络，并运用改进Elman网络对悬架试验台的液压马达进行控制；通过实验对比分析了悬架阻尼、非悬挂质量、悬架刚度、轮胎刚度等参数对轮荷利用率、相位角的影响。

基于改进免疫算法和Elman神经网络的超声马达速度辨识与控制

Identification and Speed Control of Ultrasonic Motors Based on Modified Immune Algorithm andElman Neural NetworksQiao Zhang 1, Xu Xu2, Yanchun Liang 1*1College of Computer Science and Technology, Jilin University,Key Laboratory of Symbol Computation and Knowledge Engineeringof Ministry of Education, Changchun 130012, China2College of Mathematics, Jilin University, Changchun 130012, ChinaCorresponding author: Yanchun Liang ycliang@Abstract. An improved artificial immune algorithm with a dynamic threshold ispresented in this paper. Numerical experiments show that compared with the geneticalgorithm and the originally real-valued coding artificial immune algorithm, theimproved algorithm possesses high speed of convergence and good performance ofpreventing the premature convergence. The proposed algorithm is employed to trainthe network structure, weights, initial inputs of the context units and self-feedbackcoefficient of the modified Elman network. A novel identifier and controller areconstructed successively based on the proposed algorithm. A simulated dynamicsystem of the ultrasonic motor (USM) is considered as an example of a highlynonlinear system. The novel identifier and controller are applied to perform the speedidentification and control of the ultrasonic motors. Numerical results show that boththe identifier and controller based on the proposed algorithm possesses not only highconvergent precision but also robustness to the external noise.Keywords: dynamic threshold; artificial immune algorithm; Elman network;ultrasonic motor; system identification; control.1 IntroductionThe immune system is the basic and remarkable defense system against bacteria, viruses and other disease-causing organisms. It can produce millions of antibodies from hundreds antibody genes and can protect animals which are infected by foreign molecules to survive [1-4]. The Artificial Immune System (AIS) or Artificial Immune Algorithm (AIA) was inspired by the immune system. Compared with genetic algorithm (GA), AIA has affinity calculation function, which could explain the relationship not only between the antigen and the antibody but also between antibodies. That makes AIA have the unique characteristic to guarantee the survival of the variant offspring that could match the antigen better. Related papers [5, 6] show that the algorithms based on AIA have much better performance than conventional probabilistic optimization algorithms. However, it usually takes long time for the binary coding AIA to obtain convergence. Furthermore, it is very difficult for AIA to break away from the local optimal value, which can hold the searching process around this value and can easily lead to the premature during the evolution.To improve the convergence speed and to prevent the premature convergence, a dynamic threshold artificial immune algorithm (DTAIA) is presented in this paper. The proposed algorithm changes the affinity function of the real-valued coding artificial immune algorithm by considering both the antibody’s fitness and the dynamic threshold value.An ultrasonic motor (USM) is a typical non-linear dynamic system. Due to some excellent performances and useful features, the USM has attracted considerable attention in many practical applications [7-9]. The simulation and control of the USM are important in the applications of the USM. According to the conventional control theory, an accurate mathematical model should be set up. But the USM has strongly nonlinear speed characteristics that vary with the driving conditions [10] and its operational characteristics depend on many factors. Therefore, it is difficult to perform effective identification and control to the USM using traditional methods based on mathematical models of systems. The dynamic recurrent multilayer network employs dynamic links to memorize feedback information of the history influence. It has great potential development in the fields of system modeling, identification and control [11, 12]. The Elman network is one of the simplest types among the available recurrent networks. In this paper, a modified Elman network is employed to identify and control an USM, and a novel learning algorithm based on an improved artificial immune algorithm is proposed for training the Elman network. 2 Dynamic Threshold Artificial Immune Algorithm (DTAIA)Toyoo Fukuda proposed an immune algorithm based on the information entropy, which is used to represent the diversity of the population [13]. The information entropy )(N E can be concluded as∑==M j j N HM N E 1)(1)( (1)where N is the number of the antibodies, M is the number of the genes and )(N H j is the entropy of the j th gene. The entropy )(N H j is∑==S i ij ij j P P N H 1)1log()( (2)where ij P is the probability that the i th allele comes out at the j th gene, and s is the number of the selectable characters in the alphabet.The affinity between the antibody v and w is defined as follows)2(11,H y w v +=α (3)where H (2) is the information entropy of antibody v and w .The affinity between antigen and antibody v , v x α, is defined byv v opt x =α(4)where v opt is the fitness of antibody v . Considering the model based on the Euclidean distance for affinity calculation [14], the antibody v and antibody w have the affinity if the following inequalities are satisfiedm x x l w v d w v <−<αα,),( (5)In Eq. (5), l >0 and m >0; d (v , w ) is the Euclidean distance between the antibody v and antibody w ; v x α is the fitness of antibody v ; and w x αis the fitness of antibody w .The expectation value v e of antibody v is calculated asv v v c ax e = (6)where v C is the density of antibody v . It can be seen from Eq. (6) that the antibody withboth high fitness and low density would have more chance to survive.In Ref. [14], both l and m of Eq. (5) are constants. Therefore, if two antibodies i and j have the same Euclidean distance with antibody v , but have different fitness values, then the following inequalities hold:l j v d i v d <=),(),( (7)||||j v i v ax ax ax ax −≠−, m ax ax i v <−||, m ax ax j v <−||(8) It is obvious that the one with less fitness difference is closer to the antibody v .In order to avoid such problem, we modify the inequalities of (5) and consider that if the following inequality is satisfiedL ax ax w v d w v f w v <| - |)+ ,(=） ,( (9)then the antibodies v and w have the affinity. In the improved algorithm, the value of the parameter L is an important factor for determining the density. If L is larger, there will be more antibodies that have affinity with antibody v , which makes higher density of antibody v , and the algorithm will have stronger ability to suppress antibody v to be duplicated. So the diversity will remain relatively high, vice versa.As well as we know, in the initial period of evolution, the algorithm has little possibility to fall into the local optimum value because of the high diversity. With an increase of the evolution generations, there will be more and more antibodies with high fitness values. If L is a constant, the algorithm can easily become premature and get into the local optimum since the diversity is getting lower and lower. If L is an increasing function of evolution generations, the antibody’s diversity and density will be increased efficiently with the increase of the evolution generations and that the suppression will be more powerful to preserve high diversity. So the algorithm would have strong ability to control the reproducing process. In this paper, the dynamic value of L is taken as )exp(0bT L L =, here 00>L ,0>b and T >0 is the evolution generations.Just as GA, AIA starts on the initial population that is generated randomly, and uses the reproducing, crossover and mutating operators to produce the filial generation superior to their parents. Through these iterations, the population gradually approaches to the optimum. We compared the performance of GA, MAIA (model used in Ref. [14]) and the proposed DTAIA by finding the maximum values of 1F ,3F and the minimum value of 2F .048.2,,048.2,)1()(100),,(32132122213211≤≤−+−+−=x x x x x x x x x x F(10) 12.3,,12.3,),,(3213123212≤≤−=∑=x x x x x x x F i i (11) 0,,,048.2,,048.2,)1(sin ),,(3213213163213≠≤≤−=∑=x x x x x x x x x x F i i (12)The colony size is taken as 50 ( POPSIZE=50 ), the max evolvement generation is 500, crossover probability is 0.8 and mutation probability is 0.15. Other parameters are: MAIA : m =0.2, l =0.7 at the function 1F and l =0.05 at the functions 2F and l =0.1 at the function 3F ; DTAIA : b =0.0001, 0l =0.7 at the function 1F and 0l =0.042 at the function 2F and 0l =0.04 at the function 3F .The results of the simulated experiments are shown in Table 1. From the table it can be seen that the proposed method is superior to the other two methods in terms of the convergence speed and precision.Table 1. Comparisons of GA, MAIA and DTAIAAlgorithms Function 1F Function 2F Function 3FGeneration ofreachingstable stageOptimum value Generation of reaching stable stage Optimum value Generation of reaching stable stage Optimum value GA 440 3907.966308 0.000 299 3.000 MAIA 263 3907.966208 0.000 162 3.000 DTAIA 179 3907.97092 0.000 48 3.000 3 Modified Elman network and DTAIA-based learning algorithm ofElman networkElman neural network (ENN) is a type of recurrent neural network with three layers of neurons. It includes not only input nodes, output nodes and hidden nodes, but also context nodes in this model. The context nodes are used to memorize previous activations of the hidden nodes and can be considered to function as a one-step time delay. The modified Elman network differs from the original Elman network by introducing self-feedback coefficient links to improve its memorization ability. Figure 1 depicts the modified ENN. Assume that there are r nodes in the input layer, n nodes in the hidden and context layers, respectively, and m nodes in the output layer. Then the input u is an r dimensionalvector, the output x of the hidden layer and the output c xof the context nodes are n dimensional vectors, respectively, the output y of the output layer is m dimensional vector,and the weights 1I W , 2I W and 3I W are n×n , n×r and m×n dimensional matrices, respectively. The mathematical model of the modified Elman neural network is))1()(()(−+=k u W k x W f k x I2c I1(13) )1()1()(−+−=k x k x k x c c α(14) )()(3k x W k y I = (15)where )(x f is often taken as the sigmoid function.)1/(1)(x e x f −+= (16)and α ( 10<≤α ) is the self-feedback coefficient. When the coefficient α is zero, the modified Elman network is identical to the original Elman network.Let the k th desired output of the system be )(k y d . Define the error as2/))()(())()(()(k y k y k y k y k E d T d −−=(17) Differentiating E with respect to 3I W , 2I W and 1I W respectively, according to thegradient descent method, we obtain the learning algorithm for the modified Elman neural network as follows)(033k x w j i I ij δη=Δ),..,2,1;,...,2,1(n j m i == (18))1(22−=Δk u w q h j I jq δη ),...,2,1;,...,2,1(r q n j == (19)113011)()(Ijl j m i I ij i Ijl w k x w w ∂∂=Δ∑=δη),...,2,1;,...,2,1(n l n j ==(20) where 1η, 2η and 3η are learning steps of 1I W , 2I W and 3I W respectively, and)())()((,0⋅′−=i i i d i g k y k y δ (21)∑=⋅′=m i j I ij i h j f w 130)()(δδ (22)11)1()1()()(Ijl j l j Ijl j w k x k x f w k x ∂−∂+−⋅′=∂∂α(23) If )(x g is taken as a linear function, then 1)(=⋅′i g .The network shown in Figure 1 is considered, where there exist r nodes in the input layer, n nodes in the hidden and context layers, and m nodes in the output layer. The corresponding individual structure can be illustrated in Figure 2, where ),...,(~0,01,0n C C C x x X =is a permutation of the initial inputs of the context unit, 1~I W , 2~I W and 3~I W are their respective permutations of the expansion of weight matrices 1I W , 2I W and 3I W by rows. So the number of the elements in the body is n+n×n+n×r+n×m . The individuals are trained and evolved by the proposed DTAIA algorithm.)(k 2I WFig. 1. Architecture of the Elman networkArchitecture of the individual 4 Speed identification of USM using the DTAIA-based ElmannetworkA dynamic identifier is constructed to perform the identification of non-linear systems using the DTAIA-based Elman network, which is called DTBEI. The model can be used to identify highly non-linear systems. A simulated dynamic system of the ultrasonic motor is considered as an example of a highly nonlinear system.The identification model of the motor is shown in Figure 3. Numerical simulations are performed using the model of DTBEI for the speed identification of a longitudinal oscillation USM [15] shown in Figure 4. Some parameters of the USM model are taken as: driving frequency 27.8 kHZ , amplitude of driving voltage 300 V , allowed output moment2.5 k g ·cm, rotation speed 3.8 m/ s . Fig. 3. Identification model of the motor Fig. 4. Schematic diagram of the motor The curve of the actual motor speed is shown in Figure 5 and Figure 6. A durative external moment of 1 N ·m is applied in the time window [0.3999s, 0.7s] as the externaldisturbances. Figures 7 to 12 show the identification results. The proposed DTBEI model is compared with the original Elman model using the gradient descent-based learning algorithm. The error is the difference between the identification result and the actual speed. The identification errors using the gradient descent-based learning algorithm are only less than 0.003, while the errors using the proposed method are less than 0.001. The identification error of the DTBEI is about 33.3% that of the Elman model trained by the gradient descent algorithm, and the identification precision is more than 99.9%. These results demonstrate that the proposed method can obtain higher precision and can be used to identify highly non-linear system successfully. 0.00.40.8 1.20.00.51.01.52.02.53.03.5S p e e d (m /s )Time (s) 1.040 1.042 1.044 1.0463.2643.2683.2723.276S p e e d (m /s )Time (s)Fig. 5. Actual speed curve of the USMFig. 6. Amplification of USM speed curveS p e e d (m /s)Time (s) E r r o r Time (s)Fig. 7. Speed Identification curves beforeFig. 8. Identification error curves beforethe disturbancethe disturbance S p e e d (m /s )Time (s)E r r o r Time (s) Fig. 9. Speed identification curves during Fig. 10. Identification error curves during the disturbance the disturbance S p e e d (m /s )Time (s)E r r o r Time (s)Fig. 11. Speed identification curves of the Fig. 12. Identification error curves of thestable stage stable stage 5 Speed control of USM using the DTAIA-based Elman networkA novel controller is specially designed to control non-linear systems using the DTAIA-based Elman network, which is called DTBEC. The USM used in Section 4 is still considered as an example of a highly nonlinear system to examine the performance of the controller DTBEC. In this paper the model is illustrated in Figure 13.Fig. 13. Speed control model of the motorIn the controller DTBEC, the Elman network is trained by DTAIA on line, and the driving frequency is taken as the control variable. The fitness of an individual is evaluated by22))()(/(1)(/1)(t y t y t e t f i d i i −== (24) where )(t f i is the fitness value of the individual i at sampling time t , )(t y d is the expected output at time t while )(t y i is the actual output. Figures 14 to 17 show the USM speed control curves using the DTBEC control strategy when the control speed is changed according to the sin curve and trapezoid curve respectively. From the Figure 14 and Figure 15, which is the amplification of the Figure 14 at the time windows [120s, 130s], it can be seen that the controller performs successfullyand the proposed method possesses good control precision. While from the Figure 16 and Figure 17, it also can be seen that the control model possesses rapid adaptability for the sharp change of the control speed. It suggests that the controller presented here exhibits very good robustness and can handle a variety of operating conditions without losing the ability to track a desired course well.S p e e d (m /s )Time (s)S p e e d (m /s )Time (s)Fig. 14. Speed control curves with sinusoidal Fig. 15. The amplification of the Fig. 14 attype reference speeds the time windows [120s, 130s]S p ee d (m /s )Time (s) S p e e d (m /s )Time (s) Fig. 16. Speed control curves with step Fig. 17. The amplification of the Fig. 16 at type reference speeds the time windows [2.5s, 2.8s] 6 Conclusions By analyzing the principles of artificial immune algorithm and genetic algorithm, we propose an improved immune algorithm DTAIA in order to overcome the shortage of GA and AIA for the tendency towards local optimum value and premature. Simulated experimental results show that the proposed DTAIA is more efficient than the GA algorithm and the real-valued coding AIA. The proposed algorithm DTAIA can be employed to train the Elman network and to realize effectively the evolution of network construct, weights, initial inputs of the context unit and self-feedback coefficient together. Furthermore, an identifier DTBEI and a controller DTBEC are respectively designed to identify and control non-linear systems on line. Numerical results show that the designed identifier can approximate the nonlinear input-output mapping of the USM quite well, and the controller is tested by using step and sinusoidal types speed. Both of them achieve higher convergence precision and show fairly robust characteristics. Compared with other existing approaches, the proposed methods could be effective alternatives for system identification and control, especially for non-linear dynamic systems.Acknowledgements: The authors are grateful to the support of the National Natural Science Foundation of China (60433020, 10501017), the science-technology development project of Jilin Province of China (20050705-2), the doctoral funds of the National Education Ministry of China (20030183060), and “985”project of Jilin University. References1.Hunt, J. E., Cooke, D.E.: Learning Using an Artificial Immune System. Journal of Network andComputer Applications, 19(1996) 189-2122.Wen, X., Song, A.: An Immune Evolutionary Algorithm for Sphericity Error Evaluation.International Journal of Machine Tools & Manufacture, 44 (2004) 1077-10843.Chun, J. S., Kim, M. K., Jung, H. K., Hong, S. K.: Shape Optimization of ElectromagneticDevices Using Immune Algorithm. IEEE Transactions on Magnetics, 33(1997) 1876-18794.Lun, G., Chueh, C.: Multi-objective Optimal Design of Truss Structure with ImmuneAlgorithm. Computers and Structures, 82 (2004) 829-8445.Kalinli, A., Karaboga, N.: Articificial Immune Algorithm for IIR Filter Design. EngineeringApplications of Artificial Intelligence, 18(2005) 919-9296.Chun, J. S., Jung, H. K., Hahn, S. Y.: A Study on Comparison of Optimization Performancesbetween Immune Algorithm and Other Heuristic Algorithms. IEEE Transactions on Magnetics, 34(1998) 2972-29757.Sashida, T., Kenjo, T.: An Introduction to Ultrasonic Motors. Oxford: Clarendon Press(1993)8.Ueha, S., Tomikawa, T.: Piezoelectric Motors: Theory and Application. Oxford: SciencePublications, (1993) 1 – 2939.Lin, F. J., Wai, R. J., Hong, C. M.: Identification and Control of Rotary Traveling-wave TypeUltrasonic Motor Using Neural Networks. IEEE Transactions on Control Systems Technology, 9(2001) 672-68010.Senjyu, T., Miyazato, H., Yokoda, S., Uezato, K.: Speed Control of Ultrasonic Motors UsingNeural Network. IEEE Transactions on Power Electronics, 13(1998) 381-38711.Xiong, Z. H., Zhang, J.: A Batch-to-batch Tterative Optimal Control Strategy Based onRecurrent Neural Network Models. Journal of Process Control, 15(2005) 11-2112.Cong, S., Gao, X. P.: Recurrent Neural Networks and Their Application in SystemIdentification. Systems Engineering and Electronics, 25(2003) 194-19713.Dasgupta, D.: Artificial Immune Systems and Their Applications. Berlin Heidelberg: Springer –Verlang (1999)14.Zheng, R. R., Mao, Z. Y.: A Modified Artificial Immune Algorithm. Computer Engineering andApplications, 33(2003) 55-5715.Xu, X., Liang, Y. C., Lee, H. P., Lin, W. Z., Lim, S. P., Lee, K. H.: Mechanical Modeling of ALongitudinal Oscillation Ultrasonic Motor and Temperature Effect Analysis. Smart Materials and Structures, 12(2003) 514-523。

基于改进Elman神经网络的目标威胁度预测评估

第39卷第3期2017年6月探测与控制学报Journal of Detection &ControlVol. 39 No. 3Jun. 2017基于改进Elm an神经网络的目标威胁度预测评估徐公国，段修生(解放军军械工程学院，河北石家庄0S0003)主商要：针对地面防空作战中目标威胁度难以准确评估的问题，提出了基于改进Elman神经网络的目标威胁度动态预测评估方法。

该方法利用量子粒子群智能优化(QPSO)算法对Elman神经网络进行了改进,提出了 QPSO-Elman神经网络,并基于优化的QPSO-Elman神经网络构建了目标威胁度的动态预测评估模型。

仿真分析表明，该方法有效解决了目标威胁度的动态评估问题，预测结果更加准确且实用性强，增强了防空系统的作战能力。

关键词：目标威胁度;Elman神经网络;量子粒子群优化算法;防空作战中图分类号：TP183 文献标识码：A文章编号=1008-1194(2017)03-0101-06 Target Threat Prediction Assessment Based on ImprovedElman Neural NetworkXU Gongguo,DU A N Xiusheng(Ordnance Engineering College of PLA，Shijiazhuang 050003，China) Abstract：Aiming at the problem that target threat is hard to assess in ground air defense operation, a method of target threat assessment was proposed based on the improved Elman neural network The Elman neural network was improved based on the quantum particle swarm optimization (QPSQ) , and the QPSO-Elman neural network was proposed. Besides，a assessment model was proposed based on QPSO-Elman neural network The simulation results showed that this method could effectively solved the problem, the prediction results were more accurate and practicable, and it could enhance the operational capability of the air defense system.Key words：target threat assessment；Elman neural network；quantum particle swarm optimization；air defense operation〇引言在地面防空武器系统中，特别是防空c3i系统中，目标威胁度评估是武器-目标配对问题中的关键技术。

基于相似日理论和IPSO

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《测控技术》２０２０年第３９卷第２期
ｅｒａｔｉｏｎｕｎｄｅｒｔｈｒｅｅｄｉｆｆｅｒｅｎｔｓｅａｓｏｎｓａｎｄｗｅａｔｈｅｒｃｏｎｄｉｔｉｏｎｓ．ＢａｓｅｄｏｎｔｈｅａｎａｌｙｓｉｓｏｆｐｈｏｔｏｖｏｌｔａｉｃｐｏｗｅｒｐｌａｎｔｉｎＧａｎｓｕＰｒｏｖｉｎｃｅｉｎ２０１４牞ｔｈｅｒｅｓｕｌｔｓｓｈｏｗｔｈａｔｃｏｍｐａｒｅｄｗｉｔｈｔｈｅＥｌｍａｎｍｏｄｅｌａｎｄｔｈｅＰＳＯＥｌｍａｎｍｏｄｅｌ牞ｔｈｅＩＰＳＯＥｌｍａｎｍｏｄｅｌｈａｓｂｅｔｔｅｒｐｒｅｄｉｃｔｉｏｎｅｆｆｅｃｔｓｕｎｄｅｒｄｉｆｆｅｒｅｎｔｗｅａｔｈｅｒｔｙｐｅｓ．Ｋｅｙｗｏｒｄｓ牶ｐｈｏｔｏｖｏｌｔａｉｃ牷ｐｏｗｅｒｆｏｒｅｃａｓｔ牷ｐａｒｔｉｃｌｅｓｗａｒｍｏｐｔｉｍｉｚａｔｉｏｎ牗ＰＳＯ牘ａｌｇｏｒｉｔｈｍ牷Ｅｌｍａｎｎｅｕｒａｌｎｅｔｗｏｒｋ牷ｓｉｍｉｌａｒｄａｙｔｈｅｏｒｙ

神经网络的优化与改进

神经网络的优化与改进神经网络作为人工智能的核心技术，被广泛应用于图像识别、自然语言处理、语音识别等领域。

然而，在实际应用过程中，神经网络模型存在一些问题，如模型的复杂度、训练时间、可解释性不足等。

因此，神经网络的优化与改进一直是人工智能研究人员的重要方向之一。

一、深度学习中的优化方法使用梯度下降算法来调整神经网络的权重和偏置系数是一种常见的优化方法。

在深度学习中，梯度下降算法又分为批量梯度下降算法、随机梯度下降算法和小批量梯度下降算法。

批量梯度下降算法每次使用全部的训练样本来计算梯度，然后更新权重和偏置。

这种方法的优点是稳定，但训练时间长，需要大量的存储空间。

随机梯度下降算法则是随机选择一个训练样本计算梯度并更新权重和偏置，重复这个过程直到所有样本都被用于训练。

这种方法的优点是收敛速度快，但也容易陷入局部最优解。

小批量梯度下降算法则是在样本中选择一个较小的批次来计算梯度，然后更新权重和偏置。

这种方法结合了批量梯度下降算法和随机梯度下降算法的优点，通常被广泛采用。

二、神经网络的学习率调整方法学习率是控制模型更新步长的超参数，它决定了模型的收敛速度。

学习率过高会导致模型无法收敛或直接变成震荡状态，学习率过低则会导致模型收敛时间过长。

因此，调整学习率是优化神经网络的一个重要方法。

学习率衰减是一个常用的调整方法。

在训练过程中，随着模型逐渐收敛，学习率也应相应减小。

另外，自适应学习率算法也是一个有效的方法，如AdaGrad、RMSprop、Adam等。

这些算法能够根据梯度运行时的状态自动调整学习率，以更好地适应数据变化。

三、神经网络模型的正则化方法正则化是一种常见的降低模型复杂度的方法，可以有效地避免过拟合。

常用的正则化方法包括L1正则化、L2正则化和Dropout 方法。

L1正则化和L2正则化是通过在损失函数中加入正则项对权重进行约束的方法。

L1正则化将权重向量转化为具有稀疏性质的权重向量，可以有效地减少参数数量并提升模型的泛化能力。

采用序贯学习的改进型Elman神经网络预测研究

ቤተ መጻሕፍቲ ባይዱ
ｍａｉｙｒｃｒｏｌｎｓｓｏｈｔｔｅｍｅｈｄｃｎａｃｒｔｌｅｉｔｔｅｆｔｒａａｏｌｅ，ａｄｈｓｔｅａｖｎａｅｔｃｈｄｏａｂｎｐａｔｈｗｓｔａｈｔｏａｃｕａｅｙｐｒｄｃｈｕｕｅｄｔｎｉｎｎａｈｄａｔｇｓ
ＸｕＤａｉｉｎｈｎｎｗｅ，ＪａｇＺｉｏｇ， ¨ｕＷｅｂｎｎｉ
（ｈｍｃｌａｅｎｉｅｒｇＲｓａｃｅｔｒｆｎｓｒｆｄｃｔｎ，ＢｉｎｎｖｒｉｆＣｅｉａｆｔＥｇｎｅｎｅｅｒｈＣｎｅｉｔｏｕａｉＳｙｉｏＭｉｙＥｏｅｊｇＵｉｅｓｙｏｉｔＣｅｃｌｅｈｏｏｙ，Ｂｉｎ００９，ＣｉａｈｍｉａＴｃｎｌｇｅｉｇ１０２ｊｈｎ）
化工过程在不同工作状况下，受各种随机因素影响，通过监测系统所获得的数据大多呈现出非线性时间序列的特性，利用以往比较成熟的对线性时间序列
进行建模并用模型加以预测的方式，难以对工厂的监
期故障征兆时进行准确预测、及时发现、精确诊断。文中在Ｅｍａｌｎ网络的应用中提出了两点改进，以提高
ＲｅｅｒｈＯＣｈｍｉａｓａｃＨｅｃｌＥｑｕｐｅｒｃｓｉｇＥｌａｔｒｉｍｎｔＦｏｅａｔＵｓｎｍｎＮｅｗｏｋＢａｅｎＳｑｎｉｌＬｅｒｎｇｓｄｏｅｕｅｔａａｎｉ

基于改进的Elman神经网络的网络流量预测

（＿ｓｔｎｕｐｎｓＭａａｍｅｔＯｆｉｅＣｏｌｇｆＰｎｇｉｇｈａ１ＡｓｅｓａｄＥｑｉｍｅｔｎｇｅｎｆ，ｌｅｅｏｉｄｎｓｎ，Ｐｉｇｉｇｈｎ４７０ｃｎｄｎｓａ６００）
（．Ｐｒｏｎｅｐｒｍｅ，ＣｏｌｇｆＰｉｇｉｓａ２ｅｓｎｌＤｅａｔｎｔｌｅｅｏｎｄｎｇｈｎ，Ｐｉｄｎｓａ４７０ｎｇｉｇｈｎ６００）
型，分别用Ｅｍａ１ｎ神经网络和改进的Ｅｌｎ神经网络对实际网络流量进行预测，ｍａ仿真实验结果表明，改进的Ｅｍａｌｎ神经网络具有良好的预
测效果，进的Ｅｍａ经网络具有更高的预测精度和更好的动态性能。改ｌｎ神关键词Ｅｌｎ神经网络；测；络管理ｍａ预网
ＴＰ８１３中图分类号
ＰｒｄｉｔｏｏｅｗｏｋＴｒｆｉｓｄｏｍｐｒｖｄＥｌａｅａｌＮｅｗｏｋｅｃｉｎｆｒＮｔｒａｆｃＢａｅｎＩｏｅｍｎＮｕｒｔｒ
ＷＡＮＧｎｇｉＨｏｗｅＳＵＮｉｕｎＺｈｙａ。
增长，网络规模和复杂度越来越大，网络上承载的业务和应用也日益丰富，相应的对信息网络的管理也提出了更高之的要求。网络流量的分析与预测对于大规模网络容量规
２Ｅｍａｌｎ神经网络
ＡｂｓｒｃＡｎＩｐｏｅｅｗｏｋｉｒｓｎｅａｅｎｔｍａｅａｔｒｗｉｈｄｔｏｌｃｅｒｍｈｅａｔａｅｗｏｋ，ｔｔａｔｍｒｖｄｎｔｒｓｐｅｅｔｄｂｓｄｏｈｅＥｌｎｎｕｒｌｎｅｗｏｋ，ｔａａｃｌｔｄｆｏｔｃｕｌｎｔｒａｎｅｅ

elm算法

ELM 的隐含层节点个数为 L，使 L≤P，激励函数为 g(x)。 1） 2） 3）对 ELM 的输入权值向量 wij 和隐含层节点阈值 b j 进行随机赋值，完成初始化。计算隐含层输出矩阵 H 计算输出权值矩阵 B H †T
即完成了 ELM 神经网络的建立与初始化。
%%极限学习机算法封装 function y=elm(x,t) %x 为输入矩阵，t 为输出矩阵，二者长度一样，默认 x 行数为样本组数， %x 列数为 SLFN 的输入层个数，t 的行数同 x，列数为 SLFN 的输出层个数。 %%初始化 [hang,lie]=size(x); L=hang-1; %隐含层个数，与输入样本数有关，通常能达到上百个， % 但不要大于输入样本的长度。理论上貌似是越接近，效果越好 w=rands(L,lie); b=rands(1,L); %%训练 for p=1:hang for j=1:L wx(p,j)=sum(w(j,:).*x(p,:)); H(p,j)=logsig(wx(p,j)+b(j)); end end Hn=inv(H'*H)*H'; %H 的 MP 广义逆矩阵，一般情况下 H 为行满秩矩阵（秩=行数） B=Hn*t; y=H*B;
l 1 P
络结构，其训练模型依旧可以表示为和 SLFN 一样的形式：
tl j g j (w j x b j ), l 1,..., P
j 1
L
这样的 P 个方程，可以表示为矩阵相乘的形式： HB T 其中，
1T g ( wL x1 bL ) t1T ,B ,T T T t P L g ( wL xP bL ) P L P M L M H 成为极限学习机的隐含层输出矩阵，它的每一行代表 ELM 的隐含层关于每组输入向量的输出，而每一列，对应于第 j 个隐含层关于 P 组输入向量的输出。黄广斌学者的定理证明了如果激励函数无限可导，则随机赋值的输入权值 W，

面向室内定位的DHOHF-Elman神经网络算法

第３５卷第８期２０１９年８月
文章编号：１００３－０５３０（２０１９）０８－１３５８－０８
信号处理ＪｏｕｒｎａｌｏｆＳｉｇｎａｌＰｒｏｃｅｓｓｉｎｇ
Ｖｏｌ．３５Ｎｏ．８Ａｕｇ．２０１９
面向室内定位的ＤＨＯＨＦＥｌｍａｎ神经网络算法
毛永毅阴颖
（西安邮电大学，陕西西安７１００６１）
第８期网络算法
１３５９
物的干扰造成定位精度下降，对定位目标不能达到精确的定位，已经很难满足人们的日常需求［１］。目前常用的室内定位算法有泰勒级数算法、Ｃｈａｎ算法与Ｅｌｍａｎ神经网络算法等［２６］，ＳｕｎＪｉａｎ等人提出了一种基于最小二乘法和优化算法的室内到达时间定位三维算法，通过模拟和现场测试来评估测距和定位精度［７］；ＬｉｕＹｉｎｇ等人提出了基于ＣＦＳＦＤＰ的自适应室内定位方法［８］。Ｅｌｍａｎ神经网络算法与其他定位算法相比优势显著，因此是当前国内外室内定位学者研究重点之一［９］。本文提出了ＤＨＯＨＦＥｌｍａｎ神经网络定位算法，对传统Ｅｌｍａｎ神经网络拓扑结构进行改进，较好地克服了传统Ｅｌｍａｎ神经网络算法的不足［１０］。本文选用ＩＥＥＥ８０２１５４ａ信道模型收集样本数据［１１１２］，并利用ＤＨＯＨＦＥｌｍａｎ神经网络的定位算法将测到的ＲＳＳＩ（ＲｅｃｅｉｖｅｄＳｉｇｎａｌＳｔｒｅｎｇｔｈＩｎｄｉｃａｔｉｏｎ，ＲＳＳＩ）值作为输入对网络进行训练与测试，进而提高了定位精确度和收敛速度。
引用格式：毛永毅，阴颖．面向室内定位的ＤＨＯＨＦＥｌｍａｎ神经网络算法［Ｊ］．信号处理，２０１９，３５（８）：１３５８１３６５．ＤＯＩ：１０．１６７９８／ｊ．ｉｓｓｎ．１００３０５３０．２０１９．０８．０１０．Ｒｅｆｅｒｅｎｃｅｆｏｒｍａｔ：ＭａｏＹｏｎｇｙｉ，ＹｉｎＹｉｎｇ．ＤＨＯＨＦＥｌｍａｎＮｅｕｒａｌＮｅｔｗｏｒｋＡｌｇｏｒｉｔｈｍｆｏｒＩｎｄｏｏｒＬｏｃａｌｉｚａｔｉｏｎ［Ｊ］．ＪｏｕｒｎａｌｏｆＳｉｇｎａｌＰｒｏｃｅｓｓｉｎｇ，２０１９，３５（８）：１３５８１３６５．ＤＯＩ：１０．１６７９８／ｊ．ｉｓｓｎ．１００３０５３０．２０１９．０８．０１０．

基于改进ELM神经网络的客户满意度评价模型

基于改进ELM神经网络的客户满意度评价模型卢海明;刘建鑫【摘要】使用一种动态递归网络———ELM神经网络来模拟专家打分进行电力客户满意度测评。

仿真结果表明，ELM神经网络具有训练速度快和结构简单的特点，能较准确地反映客户满意度。

同时，针对ELM神经网络基于梯度下降算法调整权值和阈值，容易陷入局部最优的缺陷，提出了利用入侵杂草算法（ IWO）优化ELM神经网络的连接权值系数。

神经网络权值优化是一个大规模多峰优化问题，已有文献证明IWO算法对于解决高维度、多峰优化问题具有明显优势。

新方法有效弥补了单一算法的不足，拥有ELM神经网络动态记忆的能力以及入侵杂草算法全局收敛性强的特点。

实例计算证明，改进ELM神经网络可以建立精度更高的电力客户满意度评价模型，保证专家评价系统的一致性和稳定性，是一种行之有效的评价方法。

%A dynamic recurrent neural network, namely ELM neural network simulating the assessment of expert scoring has been used to evaluate the electric power customer satisfaction. The calculation of real examples shows that this method is capable to reflect the lev⁃els of customer satisfaction accurately with the advantages of fast training speed and simple structure. At the same a method for optimi⁃zing the connecting weight value coefficient of ELM neural network is presented by using the global searching ability of IWO. The opti⁃mization of neural network parameters is a large scale multimodal optimization problem and the tests show that IWO has obvious advan⁃tages in solvinghigh⁃dimensional multimodal optimization problem particularly. This new approach combines the merits of ELM neural network that has the abilityof dynamic memory and the strong global searching capability of IWO which exactly makes up the shortcom⁃ings of single algorithm. The simulations reveal that neural network optimized by IWO is able to build a higher precision modal for the e⁃valuation of electric power customer satisfaction and guarantee the uniformity and stability of expert evaluating system.【期刊名称】《东北电力技术》【年(卷),期】2016(037)007【总页数】5页(P39-43)【关键词】电力客户满意度;入侵杂草算法;神经网络;电力市场【作者】卢海明;刘建鑫【作者单位】广州地铁集团有限公司运营事业总部，广东广州 510310;国网江西省电力公司萍乡供电分公司，江西萍乡 337000【正文语种】中文【中图分类】F224随着电力体制改革的不断深入及电力市场的逐步建立和完善，电力需求侧管理越来越受到供电企业的重视。

纳入雷诺数修正的GA-Elman_算法对EGTM_的研究

纳入雷诺数修正的GA-Elman 算法对EGTM 的研究赵寅 1 林文斌 1 刘博 21.中国南方航空股份有限公司工程技术分公司湖北基地湖北武汉 432200；2.中国民航大学交通科学与工程学院天津 300300摘要：为进一步减小排气温度裕度计算误差，对发动机起飞排气温度裕度基线观察值和雷诺数影响系数进行了多元非线性拟合，提出了利用雷诺数影响系数修正排气温度（ Exhaust Gas Temperature，EGT ）基线观察值的方法，将雷诺数影响系数加入神经网络的输入层，利用遗传算法（Genetic Algorithm，GA ）优化Elman 网络模型，建立排气温度裕度（Exhaust Gas Temperature Margin，EGTM ）的预测模型。

通过结合飞行数据计算，对比多元非线性拟合以及Elman 网络模型和基于Elman 网络优化的GA-Elman 模型的计算误差效果，得出实验结果：GA-Elman 对EGTM 计算精度更高，鲁棒性更强。

关键词：航空发动机排气温度裕度雷诺数基线观察值遗传算法中图分类号： V231文献标识码： A文章编号： 1672-3791(2024)02-0026-05Research of the Corrected GA-Elman Algorithm with theReynolds Number on EGTMZHAO Yin 1 LIN Wenbin 1 LIU Bo 2(1.Hubei Base of Engineering Technology Branch of China Southern Airlines Co., Ltd., Wuhan, Hubei Province,432200 China; 2.School of Transportation Science and Engineering, Civil Aviation University of China,Tianjin, 300300 China)Abstract: In order to further reduce the calculation error of exhaust gas temperature margin (EGTM), the baseline observation of the take-off EGTM and the influence coefficient of the Reynolds number of the engine are carried out multivariate nonlinear fitting, and a method of correcting the baseline observation of exhaust gas temperature (EGT) by using the influence coefficient of the Reynolds number is proposed. The influence coefficient of the Reynolds number is added to the input layer of the neural network, the Elman network model is optimized by the genetic algorithm (GA), and the prediction model of EGTM is established. By combing with flight data calculation, the calculation error effects of multivariate nonlinear fitting and the Elman network model and the GA-Elman model based on Elman network optimization are compared, and the experimental results show that GA-Elman has higher calculation accuracy and stronger robustness to EGTM.Key Words: Aeroengine; Exhaust gas temperature margin; Reynolds number; Baseline observation; Genetic algorithm排气温度裕度（Exhaust Gas Temperature Margin，EGTM ）作为反映发动机整体性能衰退情况的主要参数之一，可作为发动机性能排队、水洗、换发等工作的重要依据。