基于IGWO算法优化的SVM模拟电路故障诊断
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摘要
为提高基于支持向量机(SVM)模拟电路故障诊断的准确率和优化效率,在灰狼优化(GWO)算法的基础上,通过引入非线性收敛因子、动态权重和边界变异策略,提出了一种改进灰狼优化(IGWO)算法优化SVM参数(IGWO-SVM)的改进型分类器.首先,在Sallen-Key带通滤波器和四运放双二次高通滤波器电路中采集故障样本,并对故障样本进行小波包特征提取;然后,将特征提取后的样本划分为训练样本和测试样本,利用IGWO算法来优化SVM中的惩罚参数C和核参数γ,得到最优的SVM分类器模型;最后,与其他算法优化的SVM分类器进行对比,结果表明IGWO-SVM分类器可以防止种群陷入局部最优,同时收敛速度有了较大提升.
In order to improve the accuracy and optimization efficiency of analog circuit fault diagnosis based on support vector machine(SVM),on the basis of gray wolf optimization(GWO)algorithm,this paper proposes a modified classifier that uses the improved gray wolf optimization(IGWO)algorithm to optimize the parameter of SVM(IGSA-SVM)by introducing the nonlinear convergence factor,dynamic weight and boundary variation strategy.Firstly,the fault samples are collected in the Sallen-Key bandpass filter circuit and four opamp biquad highpass filter circuit,and wavelet packet feature extraction is applied to fault samples.Then,feature-extracted samples are divided into training samples and test samples.The IGWO algorithm is used to optimize the penalty parameter C and the kernel parametersγin SVM to obtain the optimal SVM classifier model.Finally,compared with SVM classifiers optimized by other algorithms,the results show that the IGWO-SVM classifier can prevent the population from falling into a local optimum,and the convergence speed has been greatly improved.
In order to improve the accuracy and optimization efficiency of analog circuit fault diagnosis based on support vector machine(SVM),on the basis of gray wolf optimization(GWO)algorithm,this paper proposes a modified classifier that uses the improved gray wolf optimization(IGWO)algorithm to optimize the parameter of SVM(IGSA-SVM)by introducing the nonlinear convergence factor,dynamic weight and boundary variation strategy.Firstly,the fault samples are collected in the Sallen-Key bandpass filter circuit and four opamp biquad highpass filter circuit,and wavelet packet feature extraction is applied to fault samples.Then,feature-extracted samples are divided into training samples and test samples.The IGWO algorithm is used to optimize the penalty parameter C and the kernel parametersγin SVM to obtain the optimal SVM classifier model.Finally,compared with SVM classifiers optimized by other algorithms,the results show that the IGWO-SVM classifier can prevent the population from falling into a local optimum,and the convergence speed has been greatly improved.
引文
[1]毛先柏.基于支持向量机的模拟电路故障诊断研究[D].武汉:华中科技大学,2009.
[2]刘美容,张立玮.基于小波分解和模糊聚类的模拟电路软故障诊断[J].微电子学与计算机,2014,31(12):140-143.
[3] Yuan L,He Y,Huang J,et al.A new neural-network-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor[J].IEEE Transactions on Instrumentation&Measurement,2010,59(3):586-595.
[4] Long B,Xian W,Li M,et al.Improved diagnostics for the incipient faults in analog circuits using LSSVM based on PSO algorithm with mahalanobis distance[J].Neurocomputing,2014,133(10):237-248.
[5] Chen P,Yuan L,He Y,et al.An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis[J].Neurocomputing,2016(211):202-211.
[6]梅恒荣,殷礼胜,刘冬梅,等.改进粒子群算法优化的SVM模拟电路故障诊断[J].电子测量与仪器学报,2017,31(8):1239-1246.
[7] Mirjalili S,Mirjalili S M,Lewis A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69(3):46-61.
[8]郭振洲,刘然,拱长青,等.基于灰狼算法的改进研究[J].计算机应用研究,2017,33(12):3603-3606.
[9]邓乃扬,田英杰.支持向量机:理论、算法与拓展[M].北京:科学出版社,2009.
[10]姜媛媛,韩振云,崔江.基于DAGSVC的模拟电路故障字典法[J].传感器与微系统,2011,30(4):12-16.
[2]刘美容,张立玮.基于小波分解和模糊聚类的模拟电路软故障诊断[J].微电子学与计算机,2014,31(12):140-143.
[3] Yuan L,He Y,Huang J,et al.A new neural-network-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor[J].IEEE Transactions on Instrumentation&Measurement,2010,59(3):586-595.
[4] Long B,Xian W,Li M,et al.Improved diagnostics for the incipient faults in analog circuits using LSSVM based on PSO algorithm with mahalanobis distance[J].Neurocomputing,2014,133(10):237-248.
[5] Chen P,Yuan L,He Y,et al.An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis[J].Neurocomputing,2016(211):202-211.
[6]梅恒荣,殷礼胜,刘冬梅,等.改进粒子群算法优化的SVM模拟电路故障诊断[J].电子测量与仪器学报,2017,31(8):1239-1246.
[7] Mirjalili S,Mirjalili S M,Lewis A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69(3):46-61.
[8]郭振洲,刘然,拱长青,等.基于灰狼算法的改进研究[J].计算机应用研究,2017,33(12):3603-3606.
[9]邓乃扬,田英杰.支持向量机:理论、算法与拓展[M].北京:科学出版社,2009.
[10]姜媛媛,韩振云,崔江.基于DAGSVC的模拟电路故障字典法[J].传感器与微系统,2011,30(4):12-16.