American Journal of Electrical and Electronic Engineering
ISSN (Print): 2328-7365 ISSN (Online): 2328-7357 Website: http://www.sciepub.com/journal/ajeee Editor-in-chief: Naima kaabouch
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American Journal of Electrical and Electronic Engineering. 2017, 5(3), 102-107
DOI: 10.12691/ajeee-5-3-5
Open AccessArticle

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

LingZhi Yi1, 2, Yue Liu1, 2, , WenXin Yu3 and Weihong Xiao1, 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

Pub. Date: June 02, 2017

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

Creative CommonsThis 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/

References:

[1]  Shen Y, Ding S X, Haghani A, et al. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. Journal of Process Control, 2012, 22(9): 1567-1581.
 
[2]  Henckels L, Haas R, Anderson R. Method of and apparatus for automatic fault diagnosis of electrical circuits employing on-line simulation of faults in such circuits during diagnosis: US, US 4228537 A. 1980. Bao Y, Guo Z. Study of Circuit Fault Diagnosis Method Based on BP Algorithm. Electronic Science & Technology, 2016.
 
[3]  Gui W H, Liu X Y. Fault Diagnosis Technologies Based on Artificial Intelligence for Complex Process. Basic Automation, 2002.
 
[4]  Mohammadi K, Monfared A R M, Nejad A M. Fault diagnosis of analog circuits with tolerances by using RBF and BP neural networks/ Research and Development, 2002. SCOReD 2002. Student Conference on. IEEE, 2002:317-321.
 
[5]  Moody J O, Antsaklis P J. The dependence identification neural network construction algorithm [J]. IEEE Transactions on Neural Networks, 1996, 7(1):3-15.
 
[6]  Sadeghi B H M. A BP-neural network predictor model for plastic injection molding process. Journal of Materials Processing Technology, 2000, 103(3): 411-416.
 
[7]  Gandomi A H, Yang X S, Alavi A H. Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers, 2013, 29(1): 17-35.
 
[8]  Valian E, Valian E. A cuckoo search algorithm by Lévy flights for solving reliability redundancy allocation problems. Engineering Optimization, 2013, 45(11):1273-1286.
 
[9]  Yang X S. Metaheuristic optimization: Nature-inspired algorithms and applications. Studies in Computational Intelligence, 2013, 427:405-420.
 
[10]  Zhang Z, Zhang A. A novel strategy for fault diagnosis of analog circuit online based modified kernel fuzzy C-means// IEEE International Conference on Industrial Technology. IEEE, 2016:938-943.
 
[11]  S. Guoming, W. Houjun, J. Shuyan, and L. Hong, “Fault diagnosis approach of analog circuits based on genetic wavelet neural network,” ICEMI '07. 8th International Conference on Electronic Measurement and Instruments, 2007, pp. 3-675-3-679.
 
[12]  Yu W X, Sui Y, Wang J. The Faults Diagnostic Analysis for Analog Circuit Based on FA-TM-ELM[J]. Journal of Electronic Testing, 2016(4):1-7.