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

Media IT Engineering Program

Research output: Contribution to journal › Article › peer-review

49 Scopus citations

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
State	Published - 4 Apr 2015

Keywords

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

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10.1016/j.neucom.2014.11.046

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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\}",

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AU - Choi, Hyun Duck

AU - Ahn, Choon Ki

AU - Shi, Peng

AU - Lim, Myo Taeg

AU - Song, Moon Kyou

PY - 2015/4/4

Y1 - 2015/4/4

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.

AB - 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.

KW - L-L filtering

KW - Linear matrix inequality (LMI)

KW - Takagi-Sugeno fuzzy Hopfield neural network

KW - Wirtinger-type inequality

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VL - 153

SP - 117

EP - 125

JO - Neurocomputing

JF - Neurocomputing

ER -

L₂-L_∞ Filtering for takagi-sugeno fuzzy neural networks based on wirtinger-type inequalities

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Keywords

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