Wavelet fuzzy neural network for fault diagnosis

Fuzzy neural networks show good ability of self-adaption and self-learning, wavelet transformation or analysis shows the time frequency location characteristic and multiscale ability. Inspired by these advantages, a wavelet fuzzy neural network (WFNN) is proposed for fault diagnosis in this paper. This fuzzy neural network uses the wavelet basis function as a membership function whose shape can be adjusted on line so that the networks have better learning and adaptive ability. The results of simulation show that this WFNN network method has the advantage of faster learning rate and higher diagnosis precision.

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