AN EVOLUTION-ORIENTED LEARNING ALGORITHM FOR THE OPTIMAL INTERPOLATIVE NET

An evolution-oriented learning algorithm is presented for the optimal interpolative (OI) artificial neural net recently proposed by one of the authors. The algorithm is based on a recursive least squares (RLS) training procedure. One of its key attributes is that it incorporates in the structure of the net the smallest number of prototypes from the training set T which are necessary to correctly classify all the members of T. Thus, the net grows only to the degree of complexity that it needs in order to solve a given classification problem. It is shown how this approach avoids some of the difficulties posed by the back-propagation (BP) algorithm because of the latter's inflexible network architecture. The performance of this new algorithm is demonstrated by experiments with real data, and comparisons with other existing methods are also presented.