A new adaptive polynomial neural network

This paper considers the problem of the construction of nonlinear mapping by using an adaptive polynomial neural network [1], implementing a learning rule. First we apply the method to a two-class pattern recognition problem. We use one high order neuron with a threshold element ranging from −1 to +1. Positive output means class 1 and negative output means class 2. The main idea of the method proposed is that the weights are adjusted automatically in such a way to move the decision boundary to a place of low pattern density. Once reached the convergence, to improve the generalization ability, we add a growing noise to the data available. The training is performed by a steepest-descent algorithm on the weights.