基于SFO优化SELM的模拟电路故障诊断  

Fault diagnosis of analog circuit based on SFO optimized SELM

作　　者：谈恩民[1] 李莹 TAN Enmin;LI Ying(School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin 541004,China)

机构地区：[1]桂林电子科技大学电子工程与自动化学院,广西桂林541004

出　　处：《桂林电子科技大学学报》2023年第6期501-508,共8页Journal of Guilin University of Electronic Technology

基　　金：国家自然科学基金(61741403)。

摘　　要：为了提高模拟电路故障诊断精度,解决网络隐层参数难以选择的问题,提出了一种基于旗鱼算法(SFO)优化堆叠式极限学习机(SELM)的模拟电路故障诊断算法。通过训练极限学习机自动编码器(ELM-AE)形成SELM网络,ELM-AE具有强大的表征能力,但其隐层参数的随机化会导致数据自身一部分有效特征信息丢失,并产生一些训练误差,而SFO具有收敛速度快、寻优精度高的特点,因此采用SFO寻优SELM的网络参数,使SELM具有更强的泛化能力。将两级四运放双二阶低通滤波器电路作仿真实验电路,并与遗传算法(GA)、粒子群优化算法(PSO)优化后的SELM进行比较,实验结果表明,SFO具有较强的寻优能力,可准确地对故障进行诊断,证明了该算法的可行性。In order to improve the fault diagnosis accuracy of analog circuits and solve the problem of difficult selection of network hidden layer parameters,a fault diagnosis method for analog circuits based on stacked extreme learning machine(SELM)optimized by sailfish algorithm(SFO)was proposed.The SELM network was formed by training the extreme learning machine autoencoder(ELM-AE).ELM-AE has strong characterization capabilities,but the randomization of its hidden layer parameters will lead part of the effective feature information of the data itself to the loss,and produce some training errors.However,SFO has the characteris-tics of fast convergence speed and high optimization accuracy.Therefore,SFO is used to optimize the network parameters of SELM,to make that SELM have stronger generalization ability.Finally,the two-stage four-op-amp biquad low-pass filter circuit was used as a simulated experimental circuit,and further compared with the SELM optimized by genetic algorithm(GA)and particle swarm optimization(PSO),the experimental results show that SFO has strong optimization ability and can accurately diagnose the faults,it proved the feasibility of the algorithm.

关 键 词：模拟电路 故障诊断 旗鱼算法 堆叠式极限学习机 自动编码器 

分 类 号：TN710[电子电信—电路与系统]