Design and analysis of neural networks for systems optimization

Abstract

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation. This paper presents an architecture of artificial neural networks that can be used to solve several classes of optimization problems. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. Among the problems that can be treated by the proposed approach include combinatorial optimization problems and dynamic programming problems.