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
American Journal of Electrical and Electronic Engineering.
2017,
Vol. 5 No. 3, 102-107
DOI: 10.12691/ajeee-5-3-5
Copyright © 2017 Science and Education PublishingCite this paper: LingZhi Yi, Yue Liu, WenXin Yu, 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.
Correspondence to: Yue Liu, Key Laboratory of Intelligent Computing & Information Processing Ministry of Education, Xiangtan University, XiangTan, China. Email:
1535808715@qq.comAbstract
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.
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