The Faults Diagnostic Analysis for Analog Circuit Faults Based on Cuckoo Search Algorithm and BP Neural Network

LingZhi Yi^{1, 2}, Yue Liu^{1, 2,} , WenXin Yu³ and Weihong Xiao^{1, 2}

¹Key Laboratory of Intelligent Computing & Information Processing Ministry of Education, Xiangtan University, XiangTan, China

²Wind power equipment and power conversion 2011 Collaborative Innovation Center, Xiangtan University, XiangTan, China

³School of Information and Electrical Engineering Hunan University of Science and Technology, XiangTan, China

Cite this paper

LingZhi Yi, Yue Liu, WenXin Yu and Weihong Xiao. The Faults Diagnostic Analysis for Analog Circuit Faults Based on Cuckoo Search Algorithm and BP Neural Network. American Journal of Electrical and Electronic Engineering. 2017; 5(3):102-107. doi: 10.12691/AJEEE-5-3-5

Abstract

Neural networks have many advantages, such as parallel processing, self-suit, associated memory and classify ability strongly which can be used to analog circuit fault diagnosis. But it is very easy to trap the local minimum if the initial network weights are randomly generated. To solve this problem, the cuckoo search algorithm is used to optimize the initial weights of the neural network. A novel method for analog circuit fault diagnosis is proposed in this paper, based on BP neural network as classifier optimized by cuckoo search algorithm. The feasibility and effectiveness of the proposed method are verified by the simulations of Sallen-Key low-pass filter circuit. Compared with other methods, the results show that the proposed method is effective to identify and classify faults.

Keywords

BP neural network (BP), cuckoo search algorithm (CS), analog circuit fault diagnosis

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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