Stochastic H filtering for neural networks with leakage delay and mixed time-varying delays

M. Syed Ali, R. Saravanakumar, Choon Ki Ahn, Hamid Reza Karimi

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

This paper deals with the problem of H filtering for stochastic neural networks (SNNs) with a mixed of time-varying interval delays, time-varying distributed delays, and leakage delays. A novel quintuple integral Lyapunov–Krasovskii functional (LKF) is constructed to improve the performance of the SNN. Sufficient criteria can be obtained by applying the linear matrix inequality (LMI) approach and developing a new mathematical analysis, which ensures the filtering error system is asymptotically stable in the mean square. Finally, simulation results are provided to show the superiority and usefulness of the proposed method.

Original languageEnglish
Pages (from-to)118-134
Number of pages17
JournalInformation Sciences
Volume388-389
DOIs
Publication statusPublished - 2017 May 1

Keywords

  • H filtering
  • Leakage delay
  • Linear matrix inequality
  • Stochastic neural networks
  • Time-varying delay

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Software
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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