Basis vector analyses of back-propagation neural networks

Abstract

Develops a polynomial basis function approach for modeling BP (backpropagation) neural networks. This method leads directly to a constructive proof of the BP approximation theorem. In addition, the basis vector approach provides a means to synthesize the BP neural network output as a polynomial function. An algorithm for pruning the useless basis vectors is also demonstrated.<>