Supervised learning process of multi-layer perceptron neural networks using fast least squares

Abstract

A new approach for the learning process of multilayer perceptron neural networks using a recursive-least-squares-(RLS) type algorithm is proposed. The weights in the network are updated recursively upon the arrival of a new training sample. To determine the desired target in the hidden layers an analog of the back-propagation strategy used in the conventional learning algorithms is developed. This permits the application of the learning procedure to all the other lower layers. Simulation results on the 4-b parity checker problem are provided. >