NEURAL NETWORKS FOR CONSTRAINED OPTIMIZATION PROBLEMS

This paper is concerned with utilizing neural networks and analogue circuits to solve constrained optimization problems. We propose a novel neural network architecture for solving a class of non-linear programming problems. The proposed neural network is then used, and if necessary modified, to solve minimum norm problems subject to linear constraints. Minimum norm problems have many applications in various areas, but we focus on their applications to the control of discrete dynamic processes. The applicability of the proposed neural network is demonstrated on numerical examples.