Журнал прикладной и вычислительной математики

Design Neural Network to Solve Singular Perturbation Problems

Abstract

Tawfiq LNM and Al-Abrahemee KMM

The aim of this paper is to design neural network to present a method to solve Singular perturbation problems (SPP) by using network having one hidden layer with 5 hidden units (neurons) and one linear output unit, the sigmoid activation of each hidden units is tansigmoid. The neural network trained by the back propagation with different algorithms such as quasi-Newton, Levenberg-Marquardt, and Bayesian Regulation. Finally the results of numerical experiments are compared with the exact solution in illustrative examples to confirm the accuracy and efficiency of the presented scheme.