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 journalArticlepeer-review

39 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
Pages (from-to)117-125
Number of pages9
JournalNeurocomputing
Volume153
DOIs
Publication statusPublished - 2015 Apr 4

Keywords

  • L-L filtering
  • Linear matrix inequality (LMI)
  • Takagi-Sugeno fuzzy Hopfield neural network
  • Wirtinger-type inequality

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'L<sub>2</sub>-L<sub>∞</sub> Filtering for takagi-sugeno fuzzy neural networks based on wirtinger-type inequalities'. Together they form a unique fingerprint.

Cite this