GA Optimized Binary Tree SVM for Analog Circuit Fault Diagnosis


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This paper presents a novel method of analog circuit fault diagnosis based on genetic algorithm (GA) optimized binary tree support vector machine (SVM). The real-valued coding genetic algorithm is used to optimize the binary tree structure. In optimization algorithm, we use roulette wheel selection operator, partially mapped crossover operator, inversion mutation operator. In simulation experiment, we use Monte-carlo analysis for 40kHz Sallen-Key bandpass filter and get transient response of ten faults. Then we extract feature vector by db3 wavelet packet transform and principal component analysis (PCA), and diagnose circuit faults by different SVM methods. Experiment results show the proposed method has the better classification accuracy than one-against-one (o-a-o), one-against-rest (o-a-r), Directed Acyclic Graph SVM (DAGSVM) and binary tree SVM (BT-SVM). It is suitable for practical use.



Edited by:

Yuning Zhong




B. Y. Dong and G. Ren, "GA Optimized Binary Tree SVM for Analog Circuit Fault Diagnosis", Applied Mechanics and Materials, Vol. 235, pp. 423-427, 2012

Online since:

November 2012




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