A practical Bayesian framework for backpropagation networks
Neural Computation
The world according to wavelets: the story of a mathematical technique in the making
The world according to wavelets: the story of a mathematical technique in the making
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Wavelet based fault detection in analog VLSI circuits using neural networks
Applied Soft Computing
A SVDD approach of fuzzy classification for analog circuit fault diagnosis with FWT as preprocessor
Expert Systems with Applications: An International Journal
Bearing fault diagnosis based on neural network classification and wavelet transform
WAMUS'06 Proceedings of the 6th WSEAS international conference on Wavelet analysis & multirate systems
Multiple catastrophic fault diagnosis of analog circuits considering the component tolerances
International Journal of Circuit Theory and Applications
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We have developed an analog circuit fault diagnostic system based on Bayesian neural networks using wavelet transform, normalization and principal component analysis as preprocessors. Our proposed system uses these preprocessing techniques to extract optimal features from the output(s) of an analog circuit. These features are then used to train and test a neural network to identify faulty components using Bayesian learning of network weights. For sample circuits simulated using SPICE, our neural network can correctly classify faulty components with 96% accuracy.