Applying Wavelet Support Vector Machine to Analog Circuit Fault Diagnosis

Author

Lei, Zuo ; Ligang, Hou ; Wang, Zhang ; Wuchen, Wu

Author_Institution

VLSI & Syst. Lab., Beijing Univ. of Technol., Beijing, China

Volume

1

fYear

2010

Firstpage

75

Lastpage

78

Abstract

Based on vector wavelet kernel function, a method for analog circuit diagnosis based on genetic algorithm (GA) and least squares wavelet support vector machine (LSWSVM) is proposed. Using wavelet package as a tool for extracting feature, the GA-LSWSVM is then applied to the filet circuit after training by GA; the simulation results have shown that the method can enhances the accuracy and generalization ability.

Keywords

analogue circuits; fault simulation; genetic algorithms; least mean squares methods; wavelet transforms; GA-LSWSVM; analog circuit fault diagnosis; feature extraction; genetic algorithm; kernel function; least squares wavelet support vector machine; Analog circuits; Educational technology; Fault diagnosis; Genetic algorithms; Kernel; Least squares methods; Optimization methods; Support vector machine classification; Support vector machines; Very large scale integration; GA; LSWSVM; analog circuit; fault diagnosis;

fLanguage

English

Publisher

ieee

Conference_Titel

Education Technology and Computer Science (ETCS), 2010 Second International Workshop on

Print_ISBN

978-1-4244-6388-6

Electronic_ISBN

978-1-4244-6389-3

Type

conf

DOI

10.1109/ETCS.2010.255

Filename

5459594