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Mathematical Problems in Engineering
Volume 2012, Article ID 352749, 12 pages
Research Article

Application of Extension Neural Network Type-1 to Fault Diagnosis of Electronic Circuits

Department of Electrical Engineering, National Chin-Yi University of Technology, No. 35, Lane 215, Section 1, Chung-Shan Road, Taichung County, Taiping City 411, Taiwan

Received 31 January 2012; Revised 9 March 2012; Accepted 19 March 2012

Academic Editor: Wudhichai Assawinchaichote

Copyright © 2012 Meng-Hui Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The values of electronic components are always deviated, but the functions of the modern circuits are more and more precise, which makes the automatic fault diagnosis of analog circuits very complex and difficult. This paper presents an extension-neural-network-type-1-(ENN-1-) based method for fault diagnosis of analog circuits. This proposed method combines the extension theory and neural networks to create a novel neural network. Using the matter-element models of fault types and a correlation function, can be calculated the correlation degree between the tested pattern and every fault type; then, the cause of the circuit malfunction can be directly diagnosed by the analysis of the correlation degree. The experimental results show that the proposed method has a high diagnostic accuracy and is more fault tolerant than the multilayer neural network (MNN) and the k-means based methods.