New Results on State Estimation of Static Neural Networks with Time-Varying Delays

Jing He, Yan Liang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This paper focuses on studying the H performance state estimation problem for static neural networks (SNNs) with time-varying delays. Consider the estimation problem for delayed SNNs, the previously well-known Lyapunov-Krasovski functional (LKF) methods are devoted to constructing more and more complex functionals, in which each term is positive definite function. Hence it is difficult to solve and optimize in designing estimators. In this paper, the simple delay product type LKF with negative definite terms is established for the use of the Wirtinger based inequality together with mixed convex combination approach. The delay dependent conditions in terms of linear matrix inequalities (LMIs) are obtained which lead to less conservative and more flexible estimator design results. Finally, a numerical example is given to demonstrate the merits over the existing ones.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages656-661
Number of pages6
ISBN (Electronic)9781728185262
DOIs
StatePublished - 11 Oct 2020
Event2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada
Duration: 11 Oct 202014 Oct 2020

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2020-October
ISSN (Print)1062-922X

Conference

Conference2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Country/TerritoryCanada
CityToronto
Period11/10/2014/10/20

Keywords

  • H state estimation
  • Lyapunov-Krasovski functional
  • Static neural networks
  • Time-varying delay

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