A Feedback Neural Network for Solving Nonlinear Programming Problems with Hybrid Constraints

Abstract

This paper proposes a high-performance feedback neural network model for solving nonlinear convex programming problems with hybrid constraints in real time by means of the projection method. In contrary to the existing neural networks, this general model can operate not only on bound constraints, but also on hybrid constraints comprised of inequality and equality constraints. It is shown that the proposed neural network is stable in the sense of Lyapunov and can be globally convergent to an exact optimal solution of the original problem under some weaker conditions. Moreover, it has a simpler structure and a lower complexity. The advanced performance of the proposed neural network is demonstrated by simulation of several numerical examples.