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

doi:10.1016/j.neucom.2014.11.046

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 journal › Article › peer-review

46 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 language	English
Pages (from-to)	117-125
Number of pages	9
Journal	Neurocomputing
Volume	153
DOIs	https://doi.org/10.1016/j.neucom.2014.11.046
Publication status	Published - 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

Access to Document

10.1016/j.neucom.2014.11.046

Cite this

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title = "L2-L∞ Filtering for takagi-sugeno fuzzy neural networks based on wirtinger-type inequalities",

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.",

keywords = "L-L filtering, Linear matrix inequality (LMI), Takagi-Sugeno fuzzy Hopfield neural network, Wirtinger-type inequality",

author = "Choi, {Hyun Duck} and Ahn, {Choon Ki} and Peng Shi and Lim, {Myo Taeg} and Song, {Moon Kyou}",

note = "Funding Information: This work was supported partially by the Australian Research Council ( DP140102180 , LP140100471 ) and the 111 Project ( B12018 ), partially by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF- 2014R1A1A1006101 ), partially by the Industrial Strategic Technology Development Program (No. 10041779 , Development of Energy Demand Response System for Smart Home) funded by the Ministry of Trade, Industry and Energy (MI, Korea), partially by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( NRF-2013R1A1A2060663 ), and partially by the Ministry of Trade, Industry & Energy (MOTIE), Korea Institute for Advancement of Technology (KIAT) and Honam Institute for Regional Program Evaluation through the Leading Industry Development for Economic Region. Publisher Copyright: {\textcopyright} 2014 Elsevier B.V.",

year = "2015",

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doi = "10.1016/j.neucom.2014.11.046",

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AU - Choi, Hyun Duck

AU - Ahn, Choon Ki

AU - Shi, Peng

AU - Lim, Myo Taeg

AU - Song, Moon Kyou

N1 - Funding Information: This work was supported partially by the Australian Research Council ( DP140102180 , LP140100471 ) and the 111 Project ( B12018 ), partially by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF- 2014R1A1A1006101 ), partially by the Industrial Strategic Technology Development Program (No. 10041779 , Development of Energy Demand Response System for Smart Home) funded by the Ministry of Trade, Industry and Energy (MI, Korea), partially by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( NRF-2013R1A1A2060663 ), and partially by the Ministry of Trade, Industry & Energy (MOTIE), Korea Institute for Advancement of Technology (KIAT) and Honam Institute for Regional Program Evaluation through the Leading Industry Development for Economic Region. Publisher Copyright: © 2014 Elsevier B.V.

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N2 - 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.

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