计算机工程与科学2019，Vol.41Issue(2)：246-252,7.DOI:10.3969/j.issn.1007-130X.2019.02.008
自适应狼群算法优化ELM的模拟电路故障诊断
Fault diagnosis of analog circuits based on ELM optimized by an adaptive wolf pack algorithm
摘要
Abstract
In order to detect and diagnose faulty components in analog circuits more efficiently, we propose to use the adaptive wolf pack algorithm to optimize the extreme learning machine (ELM). The method includes the adaptive genetic algorithm which effectively selects feature parameters to generate optimal feature subsets. They are then used to construct the samples which are input into the ELM network to classify the faults. Given that the connection weights between the input layer and hidden layer in the ELM network, and the deviation of the hidden layer can affect the learning speed and classification accuracy, we apply our method to optimize them and select the corresponding optimal value, thus improving the training stability of the ELM network and the success rate of fault diagnosis. The specific realization process of these methods is given through the diagnosis of two typical analog circuits, and their fault diagnosis rates are over 99％. Simulation results show that the method has good accuracy and stability for fault diagnosis of analog circuits.关键词
自适应遗传算法/极限学习机/自适应狼群算法/故障诊断/模拟电路Key words
adaptive genetic algorithm/extreme learning machine (ELM)/adaptive wolf pack algorithm/fault diagnosis/analog circuit分类
信息技术与安全科学引用本文复制引用
颜学龙,汪斌斌..自适应狼群算法优化ELM的模拟电路故障诊断[J].计算机工程与科学,2019,41(2):246-252,7.基金项目
广西自动检测技术与仪器重点实验室基金(YQ17101) （YQ17101）
