Probabilistic Neural Network Based Method for Fault Diagnosis of Analog Circuits

Authors:
Yanghong Tan;Yigang He;Meirong Liu
Affiliations:
College of electric & information engineering, Hunan University, Changsha 410082, China;College of electric & information engineering, Hunan University, Changsha 410082, China;College of electric & information engineering, Hunan University, Changsha 410082, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

A novel method for fault diagnosis of analog circuits with tolerance based on wavelet packet decomposition (WP) and probabilistic neural networks (PNN) is proposed in the paper. The fault feature vectors are extracted after feasible domains on the basis of WP decomposition of responses of a circuit is solved. Then by fusing various uncertain factors into probabilistic operations, parameters and structures of PNNs for diagnose faults are obtained based on genetic optimization method leading to best detection of faults. Finally, simulations indicated that PNN classifiers can correctly 7% more than BPNN of the test data associated with our sample circuits.