NEURAL NETWORKS FOR CONSTRAINED OPTIMIZATION PROBLEMS

This paper is concerned with utilizing neural networks and analog circuits to solve constrained optimization problems. A novel neural network architec-ture is proposed for solving a class of nonlinear programming problems. The proposed neural network, or more precisely a physically realizable approxima-tion, is then used to solve minimum norm problems subject to linear con-straints. 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. Key Words: Constrained optimization, Minimum norm problems, Analog circuit