earticle

영어

This paper investigates the uniqueness and globally uniformly asymptotic stability for a class of memristor-based recurrent neural networks with time-varying delays. By employing a homeomorphism and suitable Lyapunov functional and differential condition, a sufficient conclusion for the uniqueness and globally uniformly asymptotic stability of a class of memristor-based recurrent neural networks is attained. Comparing with the previous corresponding results, we can derive that our results are new and improve the previous result reported on global uniform asymptotic stability. Two illustrative examples are given to demonstrate the applicability and advantages of our result.