An error passivation approach to filtering for switched neural networks with noise disturbance

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

In this paper, an error passivation approach is used to derive a new passive and exponential filter for switched Hopfield neural networks with time-delay and noise disturbance. Based on Lyapunov-Krasovskii stability theory, Jensen's inequality, and linear matrix inequality (LMI), a new sufficient criterion is established such that the filtering error system is exponentially stable and passive from the noise disturbance to the output error. It is shown that the unknown gain matrix of the proposed switched passive filter can be determined by solving a set of LMIs, which can be easily facilitated by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed switched passive filter.

Original languageEnglish
Pages (from-to)853-861
Number of pages9
JournalNeural Computing and Applications
Volume21
Issue number5
DOIs
Publication statusPublished - 2012 Jul 1
Externally publishedYes

    Fingerprint

Keywords

  • Exponential filter
  • Linear matrix inequality (LMI)
  • Lyapunov-Krasovskii stability theory
  • Passive filter
  • Switched Hopfield neural networks

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Cite this