Fault Diagnosis Method of Analog Circuit Based on GA-OS-ELM

摘要

Online Sequential Extreme Learning Machine (OS-ELM) can help to achieve high diagnostic accuracy, strong generalization performance, and high efficiency in processing big data, so this algorithm is widely used in the field of classification. However, parameters in the two hidden-layers, i.e., the input weight coefficients and thresholds are randomly set. Once the parameters are not feasible, the performance of the classification model will severely degenerate. In order to solve this problem, this paper combines OS-ELM and GA. Based on the GA, the optimal parameters of the hidden-layer are searched for better performance of the online sequential limit learning machine. By comparing the classification accuracy of the algorithm and the traditional online sequential limit learning machine algorithm in the fault diagnosis of the liquad filter of the analog circuit, it is found that the online sequential limit learning machine algorithm based on genetic algorithm can significantly improve the accuracy of the model.