A general projection neural network for solving optimization and related problems

Abstract

In this paper, we propose a general projection neural network for solving a wider class of optimization and related problems. In addition to its simple structure and low complexity, the proposed neural network include existing neural networks for optimization, such as the projection neural network, the primal-dual neural network, and the dual neural network, as special cases. Under various mild conditions, the proposed general projection neural network is shown to be globally convergent, globally asymptotically stable, and globally exponentially stable. Furthermore, several improved stability criteria on two special cases of the general projection neural network are obtained under weaker conditions. Simulation results demonstrate the effectiveness and characteristics of the proposed neural network.