Global exponential stability of discrete-time neural networks for constrained quadratic optimization

Abstract

A class of discrete-time recurrent neural networks for solving quadratic optimization problems over bound constraints is studied. The regularity and completeness of the network are discussed. The network is proven to be globally exponentially stable (GES) under some mild conditions. The analysis of GES extends the existing stability results for discrete-time recurrent networks. A simulation example is included to validate the theoretical results obtained in this letter.