A new training method for solving the XOR problem

Abstract

Training of Artificial Neural Networks (ANN) is an important step to make the network able to accomplish the desired task. This capacity of learning in such networks makes them applied in many applications as modeling and control. However, many of training algorithms have some drawbacks like: too many parameters to be estimated, important calculus time. In this paper, we propose a very simple method to train a Single Hidden Layer Perceptron (SHLP) based on replacing the traditional ANN’s phase training by another approach called Neural Least Mean Square (NLMS) problem resolution. The key of this method is to compute some ANN’s weights by the Least Mean Square (LMS) formula, and to leave others weights to their initial values. This new training method is applied to the classical XOR problem and the results are compared with the conventional Backpropagation algorithm. The obtained results were satisfactory and the comparison made with the classical algorithm revealed that our method allowed to reduce several parameters in the learning, namely: the computation time, the overall value of the error squared, the number of iterations and the number of weights to be adjusted