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This paper focuses on the potential of GA algorithm for adaptive random global search, and WNN resolution as well asthe ability of fault toleranceto build a multi intelligent algorithm based on the GA-WNN model using the filter unit of analog circuit forfault diagnosis. Construction of GA-WNN model was divided into two stages; in the first stage GA was used to optimize theinitial weights, threshold, expansion factor and translation factorof WNN structure; while in the second stage, initially, based on WNN training and learning, global optimal solution was obtained. In the process of usinganalog output signal by using wavelet decomposition, the absolute value of coefficient of each frequency band sequence was obtained along with the energy characteristics of the cross joint, with acombination of feature vectors as the input of the neural network. Through the pretreatment method, in order to reduce the neural network input, neural grid size of neurons was reduced in each layer and the convergence speed of neural network was increased. The experimental results show that the method can diagnose single and multiple soft faults of the circuit, with high speed and high precision.

Application Research on Fault Diagnosis of the Filter Unit Based on Intelligent Algorithm of GA and WNN