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Estimation of a class of stochastic switching neural networks with sensor saturations through a nonsynchronous filter

  • Harbin Institute of Technology
  • Ohio State University
  • Western Sydney University

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

Abstract

In this paper, the problem of energy-to-peak state estimation for a class of discrete-time Markov jump recurrent neural networks (RNNs) with randomly occurring sensor saturations is investigated. A practical phenomenon of nonsynchronous jumps between RNNs modes and desired mode-dependent filters is considered and a nonstationary mode transition among the filters is used to model the non-synchronous jumps to different degrees that are also mode-dependent. The sensor saturation occurs in a probabilistic way according to a Bernoulli sequence. Sufficient conditions on the existence of the nonsynchronous filters are obtained such that the filtering error system is stochastically stable and achieves a prescribed energy-to-peak performance index. A numerical example is presented to verify the theoretical findings.

Original languageEnglish
Title of host publicationProceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2202-2207
Number of pages6
EditionMarch
ISBN (Electronic)9781479958252
DOIs
StatePublished - 2 Mar 2015
Event2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 - Shenyang, China
Duration: 29 Jun 20144 Jul 2014

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)
NumberMarch
Volume2015-March

Conference

Conference2014 11th World Congress on Intelligent Control and Automation, WCICA 2014
Country/TerritoryChina
CityShenyang
Period29/06/144/07/14

Keywords

  • Markov jump recurrent neural networks (RNNs)
  • Nonstationary Markov chain
  • Nonsynchronous filter
  • Randomly occurring sensor saturation

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