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Wavelet analysis based analog circuit fault detecting sensitivity and feature extraction optimization methods are studied. Good localization on time-frequency domain of wavelet transformation provides better feature representation for faulty circuits than unitary time or frequency domain analysis. However, different wavelets express diverse resolution for fault recognition. Root mean square (RMS)...
The selection of parameters and structure are critical for wavelet neural networks (WNN) when they are used for fault diagnosis. Genetic algorithm is presented to optimize the structure and the parameters of WNN in the training process because of its good ability of global optimization. This method solves the main problem of easily falling into local extreme minimum to cause slow convergence when...
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