Design of delay-range-dependent state estimators for discrete-time recurrent neural networks with interval time-varying delay

Chien-Yu Lu, Jui Chuan Cheng, Te Jen Su

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper performs a global stability analysis of a particular class of recurrent neural networks (RNN) with time-varying delay. Both Lipschitz continuous activation functions and monotone nondecreasing activation functions are considered. Globally delay-dependent robust stability criteria are derived in the form of linear matrix inequalities (LMI) through the use of Leibniz-Newton formula and relaxation matrices. Finally, two numerical examples are given to illustrate the effectiveness of the given criterion.

Original languageEnglish
Title of host publication2008 American Control Conference, ACC
Pages4209-4213
Number of pages5
DOIs
Publication statusPublished - 2008 Sep 30
Event2008 American Control Conference, ACC - Seattle, WA, United States
Duration: 2008 Jun 112008 Jun 13

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2008 American Control Conference, ACC
CountryUnited States
CitySeattle, WA
Period08-06-1108-06-13

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All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Lu, C-Y., Cheng, J. C., & Su, T. J. (2008). Design of delay-range-dependent state estimators for discrete-time recurrent neural networks with interval time-varying delay. In 2008 American Control Conference, ACC (pp. 4209-4213). [4587154] (Proceedings of the American Control Conference). https://doi.org/10.1109/ACC.2008.4587154