Using Feed Forward Neural Network to Solve Eigenvalue Problems

Abstract EN

The aim of this paper is to presents a parallel processor technique for solving eigenvalue problem for ordinary differential equations using artificial neural networks.

The proposed network is trained by back propagation with different training algorithms quasi-Newton, Levenberg-Marquardt, and Bayesian Regulation.

The next objective of this paper was to compare the performance of aforementioned algorithms with regard to predicting ability.