Exponential stability of complex-valued memristor-based neural networks with time-varying delays

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)222-234
Journal / PublicationApplied Mathematics and Computation
Volume313
Online published11 Jul 2017
Publication statusPublished - 15 Nov 2017

Abstract

In this paper, we propose a new type of complex-valued memristor-based neural networks with time-varying delays and discuss their exponential stability. Firstly, by using a matrix measure method, the Halanay inequality and some analytic techniques, we derive a sufficient condition for the global exponential stability of this type of neural networks. Then, we build a Lyapunov functional and utilize the Halanay inequality to establish several criteria for the exponential stability of such networks with time-varying delays. Finally, we show two numerical simulations to demonstrate the theoretical results.

Research Area(s)

  • Complex-valued network, Exponential stability, Lyapunov–Krasovskii functional, Matrix measure, Memristor-based neural network