In this paper, two improved BP neural network algorithms of fault diagnosis for analog circuit are presented through using optimal wavelet packet transform(OWPT) or incomplete wavelet packet transform(IWPT) as preprocessor. The purpose of preprocessing is to reduce the nodes in input layer and hidden layer of BP neural network, so that the neural network gains faster training and convergence sp...

This paper presents a discrete wavelet transform and neural network approach to fault detection and classification in transmission lines. The detection and classification is carried out by using energy of the detail coefficients of the phase signals, used as input to neural network to classify the faults on transmission lines. Neural network perform well when faced with different fault conditio...

This paper presents a discrete wavelet transform and neural network approach to fault detection and classification in transmission line faults. The detection is carried out by the analysis of the details coefficients energy of the phase signals, and as an input to neural network to classify the faults on transmission lines. Neural network perform well when faced with different fault conditions ...

Since past few years, researchers have been concentrating on the classification of Electromyography Signal. This method is very useful in diagnosing the neuro-muscular disorders, which consists of wide spread diseases affecting peripheral nervous system. Progressive muscle weakness is the major form of these disorders. Out of various proposed methods, scholars are commonly focusing on Neural Ne...

This paper presents a genetic algorithm based approach for designing fuzzy wavelet neural network (FWNN) and its application for control of dynamic plants. The structure of the proposed Fuzzy WNN consists of combination of two network structure. Upper side contains wavelet neural networks and down side contains network structure of the fuzzy inference system. A simple genetic algorithm is appli...