Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Gradient-Based Optimization of Hyperparameters
Neural Computation
Feature Selection with Kernel Class Separability
IEEE Transactions on Pattern Analysis and Machine Intelligence
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
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We have developed a neural-network-based fault diagnosis approach of analog circuits using maximal class separability based kernel principal components analysis (MCSKPCA) as preprocessor. The proposed approach can detect and diagnose faulty components efficiently in the analog circuits by analyzing their time responses. First, using wavelet transform to preprocess the time responses obtains the essential and reduced candidate features of the corresponding response signals. Then, the second preprocessing by MCSKPCA further reduces the dimensionality of candidate features so as to obtain the optimal features with maximal class separability as inputs to the neural networks. This simplifies the architectures reasonably and reduces the computational burden of neural networks drastically. The simulation results show that our resulting diagnostic system can classify the faulty components of analog circuits under test effectively and achieves a competitive classification performance.