期刊名称：American Journal of Electrical and Electronic Engineering
印刷版ISSN：2328-7365
电子版ISSN：2328-7357
出版年度：2017
卷号：5
期号：3
页码：102-107
DOI：10.12691/ajeee-5-3-5
语种：English
出版社：Science and Education Publishing
摘要：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.
