The improved Harris Hawks optimization algorithm (IHHO) is proposed to solve the problem that analog circuit fault diagnosis is difficult due to multiple fault types,unstable fault states and redundant fault data. IHHO optimized back propagation (BP) neural network to realize fault feature selection and diagnosis of analog circuits. Firstly,the nonlinear adaptive factor,Cauchy variation and stochastic difference perturbation are introduced into the Harris Hawks optimization algorithm to improve the convergence speed and accuracy. Secondly,IHHO is used to select the characteristics of the single fault and the combined fault simulation data of the analog circuit to complete the data preprocessing. Finally,IHHO-BP algorithm is used to train and test the preprocessed fault data to realize the fault diagnosis of analog circuits. The diagnostic results show that the proposed method improves the diagnostic accuracy by 5.5％ compared with other algorithms.
关键词
模拟电路/特征选择/故障诊断/改进哈里斯鹰算法/反向传播神经网络
Key words
analog circuit/feature selection/fault diagnosis/improved Harris Hawks optimization/back propagation neural network
维普
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