ANALOG CIRCUITS FAULT DIAGNOSIS USING GENETIC ALGORITHM BASED WAVELET NEURAL NETWORK

Abstract

In analog circuits, fault diagnosis is a complex task due to lack of simple fault models and the presence of component tolerances and circuit non- linearity. The method of fault diagnosis in analog circuits using Genetic Algorithm (GA) based Wavelet Neural Network (WNN) is presented in this paper. Wavelet Neural Networks are a new class of networks that combine the classic Neural Networks (NNs) and Wavelet Transform (WT) which inherits the advantages of the Neural Network and wavelet transformation. In this work Wavelet Neural Network is constructed and the network weights are updated to get the desired value at the output nodes of WNN. The constructed WNN is trained with training set until the objective function is minimized and the faults are classified. Then the parameters of WNN are optimized using Genetic Algorithm (GA). The simulation results obtained for WNN and GA based WNN are compared. A comparison of the proposed work of GA based WNN with WNN reveals that the proposed method performs significantly better in fault diagnosis of analog circuits with improved classification accuracy