An on-line hybrid learning algorithm for multilayer perceptron in identification problems

Abstract

A hybrid learning algorithm for multilayered perceptrons (MLPs) and pattern-by-pattern training, based on optimized instantaneous learning rates and the recursive least squares method, is proposed. This hybrid solution is developed for on-line identification of process models based on the use of MLPs, and can speed up the learning process of the MLPs substantially, while simultaneously preserving the stability of the learning process. For illustration and test purposes the proposed algorithm is applied to the identification of a non-linear dynamic system.