L2-L Filtering for Takagi-Sugeno fuzzy neural networks based on Wirtinger-type inequalities

Hyun Duck Choi, Choon Ki Ahn, Peng Shi, Myo Taeg Lim, Moon Kyou Song

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

This paper deals with the L2-L∞ filtering problem for continuous-time Takagi-Sugeno fuzzy delayed Hopfield neural networks based on Wirtinger-type inequalities. A new set of delay-dependent conditions is established to estimate the state variables of fuzzy neural networks through the observed input and output variables. This ensures that the state estimation error system is asymptotically stable with a guaranteed L2-L∞ performance. The presented criterion is formulated in terms of linear matrix inequalities (LMIs). An example with simulation results is given to illustrate the effectiveness of the proposed fuzzy neural state estimator.

Original languageEnglish
JournalNeurocomputing
DOIs
Publication statusAccepted/In press - 2014 Jul 11

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Cognitive Neuroscience

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