Fault daignosis based on AGA-LS-SVM for analog circuit

Abstract

Fault diagnosis is very important for development and maintenance of safe and reliable electronic circuits and systems. This paper describes an approach of soft fault diagnosis for analog circuits based on least square support vector machines (LS-SVM) and adaptive genetic algorithm (AGA), known as AGA-LS-SVM. AGA is applied to optimize the parameters of LS-SVM, and fault features are extracted from the frequency domain response of circuit under test (CUT) and the LS-SVM which trained by the fault features is used to recognize the unknown faults. The experimental results demonstrate that LS-SVM optimized by AGA performs better forecast accuracy and successful modeling of diagnosing analog circuits fault.