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A novel method of stimuli design for analogue circuit fault diagnosis is presented based on a genetic algorithm and wavelet packet decomposition. The method is characterised by reduced fault ambiguity and more accurate fault classification. Computation is also reduced because the method does not require matrix inversion and calculation. The proposed method is suitable for transient and AC input sources and can be applied to both linear and nonlinear circuits. [ABSTRACT FROM AUTHOR]

A novel method for fault diagnosis of analog circuits with tolerance based on wavelet packet (WP) decomposition and probabilistic neural networks using genetic algorithm (GPNN) is proposed in this paper. The fault feature vectors are extracted after feasible domains on the basis of WP decomposition of responses of a circuit being solved. Then by fusing various uncertain factors into probabilistic operations, GPNN methods to diagnose faults are proposed whose parameters and structure obtained form genetic optimisations resulting in best detection of faults. Finally, simulations indicated that GPNN classifiers are correct 7% more than BPNN of the test data associated with our sample circuits. [ABSTRACT FROM AUTHOR]