One of the major difficulties in neural network applications is the selection of the parameters in network configuration and the coefficients in learning rule for fast convergence. This paper develops a network design by combining the Taguchi method and the back-propagation network with an adaptive learning rate for minimum training time and effective vibration suppression. Analyses and experiments show that the optimal design parameters can be determined in a systematic way thereby avoiding the lengthy trial-and-error.

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