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WNN Fault Diagnosis Model with Modified Parameter Initialization for Analog Circuit

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DOI: 10.23977/icamcs.2018.047


Yanming Wei, Baohua Zhang, Jingjuan Sun

Corresponding Author

Yanming Wei


To diagnose the fault in analog circuit correctly, a Wavelet Neural Network (WNN) method is proposed with improvement of parameter initialization. Considering the shortcoming of traditional randomization method of WNN initialization, a new initialization method is introduced to improve the capability of WNN model, which considers not only the initialization of connection weights, but also the initialization of the expansion factor and translation factor. The simulation shows that the proposed method has a good diagnosis capability for the fault in analog circuit.


Wavelet neural network, parameter initialization, analog circuit, fault diagnosis

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