A high performance neural network for solving nonlinear programming problems with hybrid constraints

A continuous neural network is proposed in this Letter for solving optimiza tion problems. It not only can solve nonlinear programming problems with th e constraints of equality and inequality, but also has a higher performance . The main advantage of the network is that it is an extension of Newton's gradient method for constrained problems, the dynamic behavior of the netwo rk under special constraints and the convergence rate can be investigated. Furthermore, the proposed network is simpler than the existing networks eve n for solving positive definite quadratic programming problems. The network considered is constrained by a projection operator on a convex set. The ad vanced performance of the proposed network is demonstrated by means of simu lation of several numerical examples. (C) 2001 Elsevier Science B.V. All ri ghts reserved.