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A linear ridgelet network combining ridgelet, linear term and the standard feed-forward neural network used for diagnosing the faults of analog circuit is constructed, and a training algorithm based on the steepest gradient descent method and momentum method for this network and the procedure for diagnosing these faults are proposed. The resulting linear ridgelet network can learn more rapidly from training samples and handle more effectively the complicated fault information of circuit under test than wavelet network and ridgelet network, classifying these faults effeciently and correctly and achieving a high classification accuracy. The simulation results demonstrate the effectiveness of this approach.
A linear ridgelet network combining ridgelet, linear term and the standard feed-forward neural network used for diagnosing the faults of analog circuit is constructed, and a training algorithm based on the steepest gradient descent method and momentum method for this network and the procedure for diagnosing The resulting linear ridgelet network can learn more rapidly from training samples and handle more effectively the complicated fault information information of circuit under test than wavelet network and ridgelet network, classifying these faults effeciently and correctly and achieving a high classification accuracy. The simulation results demonstrates the effectiveness of this approach.