Passivity of switched recurrent neural networks with time-varying delays

Jie Lian, Jun Wang

Research output: Contribution to journalArticlepeer-review

110 Citations (Scopus)

Abstract

This paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time-varying delays. First, using the multiple Lyapunov functions method, a state-dependent switching law is designed to present a stochastic passivity condition. Second, a hysteresis switching law involving both the current state and the previous value of the switching signal are presented to avoid chattering resulted from the state-dependent switching. Third, based on the average dwell-time approach, a class of switching signals is determined to guarantee the switched neural network stochastically passive. Finally, three numerical examples are provided to illustrate the characteristics of three proposed switching laws.

Original languageEnglish
Article number7001700
Pages (from-to)357-366
Number of pages10
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume26
Issue number2
DOIs
Publication statusPublished - 1 Feb 2015
Externally publishedYes

Keywords

  • Average dwell time
  • hysteresis switching law
  • passivity
  • switched neural networks.

Fingerprint

Dive into the research topics of 'Passivity of switched recurrent neural networks with time-varying delays'. Together they form a unique fingerprint.

Cite this