On the effect of the nonlinearity of the sigmoid function in artificial neural network classifiers

Despite a considerable amount of recent directed towards pattern recognition applications of the artificial neural networks (ANNs), little quantitative information concerning the nonlinearity of the sigmoid function is available to a designer. In this paper, the authors study the effect of the nonlinearity of the sigmoid function on the generalization capability of the ANN classifiers trained with the backpropagation algorithm. Experimental results show that like the case of the network size, there exists the optimal degree of the nonlinearity for a particular problem