Gain-Scheduled Finite-Time Synchronization for Reaction-Diffusion Memristive Neural Networks Subject to Inconsistent Markov Chains

Xiaona Song, Jingtao Man, Shuai Song, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

An innovative class of drive-response systems that are composed of Markovian reaction-diffusion memristive neural networks, where the drive and response systems follow inconsistent Markov chains, is proposed in this article. For this kind of nonlinear parameter-varying systems, a suitable gain-scheduled controller that involves a mode and memristor-dependent item is designed, so that the error system is bounded within a finite-time interval. Moreover, by constructing a novel Lyapunov-Krasovskii functional and employing the canonical Bessel-Legendre inequality and free-weighting matrix method, the conservatism of the finite-time synchronization criterion can be greatly reduced. Finally, two numerical examples are provided to illustrate the feasibility and practicability of the obtained results.

Original languageEnglish
Article number9153952
Pages (from-to)2952-2964
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume32
Issue number7
DOIs
Publication statusPublished - 2021 Jul

Keywords

  • Canonical Bessel-Legendre (B-L) inequality
  • finite-time synchronization
  • gain-scheduled controller
  • inconsistent Markov chains
  • Markovian reaction-diffusion memristive neural networks (MNNs)

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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