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 parameters γ in 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.
作者
熊魁
岳长喜
刘冬梅
梅恒荣
XIONG Kui;YUE Chang-xi;LIU Dong-mei;MEI Heng-rong(China Electric Power Research Institute,Wuhan 430074,China;School of Electrical Engineering and Automation,Hefei University of Technology,Hefei 230009,China)
出处
《微电子学与计算机》
北大核心
2019年第1期16-21,共6页
Microelectronics & Computer
基金
国家电网公司科技项目(JL71-18-003)
关键词
改进灰狼优化算法
支持向量机
模拟电路
故障诊断
improved gray wolf optimization algorithm
support vector machine
analog circuit
fault diagnosis
作者简介
熊魁,男,(1988一),硕士,工程师.研究方向为电气测量技术;岳长喜,男,(1982一),硕士,高级工程师.研究方向为高电压大电流以及电能计量技术;通讯作者:刘冬梅,女,(1982一),博士,副教授,硕士生导师.研究方向为电路设计、电路故障诊断和信号处理.E-mail:dmliul00@hfut.edu.cn;梅恒荣,男,(1993一),硕士研究生.研究方向为机器学习和故障诊断.
