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Using a Sensitivity Measure to Improve Training Accuracy and Convergence for Madalines

Madalines with discrete input, output and activation function are suitable for solving many inherently discrete problems and meanwhile are more facile for implementing and less complex for computing than their continuous counterparts. However, there has not yet been efficient training algorithm for Madalines. By now the most popular one must be the MRII proposed by Winter and Widrow [1] [2]. In this paper, based on the MRII, we present a new algorithm to improve the training accuracy and convergence for Madalines. In our algorithm, a sensitivity measure is used to replace the confidence measure used in MRII so as to better satisfy the minimal disturbance principle. Computer simulations are run to verify the effects of our training algorithm. The experimental verification shows that our algorithm has higher success rate and faster convergence speed than the MRII.