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In applying the neural network to the classification problem in pharmacology, we adopt an extended back-propagation (EBP) learning which adjusts the parameters appearing in an activation function, as well as the weights. The results of simulations show that such an extended learning speeds up the learning process as compared with the conventional basic back-propagation procedure, irrespective of the initial values of the parameters, which is extremely useful in the practical application of the neural network in the pharmaceutical field. We have also found that use of Morita's activation function beyond the sigmoid type further accelerates the EBP learning in some cases.

An Improvement of Neural Networks Applied to Pharmaceutical Problems.