Passive and exponential filter design for fuzzy neural networks

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48 Citations (Scopus)

Abstract

This paper proposes a new passive and exponential filter for Takagi-Sugeno fuzzy Hopfield neural networks, with time delay and external disturbance. Based on the Lyapunov-Krasovskii stability theory, Jensen's inequality, and linear matrix inequality (LMI), a new delay-dependent criterion is proposed such that the filtering error system becomes exponentially stable and passive from the external disturbance to the output error. The proposed filter can be obtained by solving the LMI, which can be easily facilitated using standard numerical packages. Two numerical examples are given to illustrate the effectiveness of the proposed filter.

Original languageEnglish
Pages (from-to)126-137
Number of pages12
JournalInformation Sciences
Volume238
DOIs
Publication statusPublished - 2013 Jul 20

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Keywords

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

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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