Stability Analysis of Discrete-Time Neural Networks with Time-Varying Delay via an Extended Reciprocally Convex Matrix Inequality

Chuan Ke Zhang, Yong He, Lin Jiang, Qing Guo Wang, Min Wu

Research output: Contribution to journalArticlepeer-review

225 Citations (Scopus)

Abstract

This paper is concerned with the stability analysis of discrete-time neural networks with a time-varying delay. Assessment of the effect of time delays on system stability requires suitable delay-dependent stability criteria. This paper aims to develop new stability criteria for reduction of conservatism without much increase of computational burden. An extended reciprocally convex matrix inequality is developed to replace the popular reciprocally convex combination lemma (RCCL). It has potential to reduce the conservatism of the RCCL-based criteria without introducing any extra decision variable due to its advantage of reduced estimation gap using the same decision variables. Moreover, a delay-product-type term is introduced for the first time into the Lyapunov function candidate such that a delay-variation-dependent stability criterion with the bounds of delay change rate is established. Finally, the advantages of the proposed criteria are demonstrated through two numerical examples.

Original languageEnglish
Article number7858742
Pages (from-to)3040-3049
Number of pages10
JournalIEEE Transactions on Cybernetics
Volume47
Issue number10
DOIs
Publication statusPublished - Oct 2017

Keywords

  • Delay-product-type Lyapunov function
  • discrete-time neural networks
  • extended reciprocally convex matrix inequality
  • interval time-varying delay
  • stability

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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