Estimator design for discrete-time switched neural networks with asynchronous switching and time-varying delay

Dan Zhang, Li Yu, Qing Guo Wang, Chong Jin Ong

Research output: Contribution to journalArticlepeer-review

126 Citations (Scopus)

Abstract

This brief deals with the estimator design problem for discrete-time switched neural networks with time-varying delay. One main problem is the asynchronous-mode switching between the neuron state and the estimator. Our goal is to design a mode-dependent estimator for the switched neural networks under average dwell time switching such that the estimation error system is exponentially stable with a prescribed l2 gain (in the H sense) from the noise signal to the estimation error. A new Lyapunov functional is constructed that may increase during the mismatched switchings. New results on the stability and l2 gain analysis are then obtained. The admissible estimator gains are computed by solving a set of linear matrix inequalities. The relations among the switching law, the maximal delay upper bound, and the optimal H disturbance attenuation level are established. The effectiveness of the proposed design method is finally illustrated by a numerical example.

Original languageEnglish
Article number6157631
Pages (from-to)827-834
Number of pages8
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume23
Issue number5
DOIs
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • Asynchronous switching
  • average dwell time
  • state estimation
  • switched neural networks
  • time-varying delay

ASJC Scopus subject areas

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

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