H∞ filtering for discrete-time fuzzy stochastic neural networks with mixed time-delays

Yajun Li; Wenping Xiao; Jingzhao Li; Like Jiao

doi:10.1007/s12190-015-0926-2

H_∞ filtering for discrete-time fuzzy stochastic neural networks with mixed time-delays

Yajun Li^*
, Wenping Xiao
, Jingzhao Li
, Like Jiao

^*Corresponding author for this work

Shunde Polytechnic

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

6 Scopus citations

Abstract

The H_∞ filter problem for a class of fuzzy stochastic discrete neural networks system with mixed delays is studied in this paper. The mixed delays consist of discrete and distributed delays. Based on discrete inequality technic and the Lyapunov–Krasovskii functional approach, sufficient conditions for the existence of admissible filters are established in terms of linear matrix inequalities, which ensure the asymptotical mean-square stability as well as a prescribed H_∞ disturbance attenuation level. Examples and simulations are provided to illustrate the effectiveness of the proposed method.

Original language	English
Pages (from-to)	1-26
Number of pages	26
Journal	Journal of Applied Mathematics and Computing
Volume	52
Issue number	1-2
DOIs	https://doi.org/10.1007/s12190-015-0926-2
State	Published - 1 Oct 2016
Externally published	Yes

Keywords

Asymptotically mean square stable
Fuzzy systems
H filtering
Linear matrix inequality(LMI)
Stochastic discrete neural networks

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10.1007/s12190-015-0926-2

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title = "H∞ filtering for discrete-time fuzzy stochastic neural networks with mixed time-delays",

abstract = "The H∞ filter problem for a class of fuzzy stochastic discrete neural networks system with mixed delays is studied in this paper. The mixed delays consist of discrete and distributed delays. Based on discrete inequality technic and the Lyapunov–Krasovskii functional approach, sufficient conditions for the existence of admissible filters are established in terms of linear matrix inequalities, which ensure the asymptotical mean-square stability as well as a prescribed H∞ disturbance attenuation level. Examples and simulations are provided to illustrate the effectiveness of the proposed method.",

keywords = "Asymptotically mean square stable, Fuzzy systems, H filtering, Linear matrix inequality(LMI), Stochastic discrete neural networks",

author = "Yajun Li and Wenping Xiao and Jingzhao Li and Like Jiao",

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T1 - H∞ filtering for discrete-time fuzzy stochastic neural networks with mixed time-delays

AU - Li, Yajun

AU - Xiao, Wenping

AU - Li, Jingzhao

AU - Jiao, Like

PY - 2016/10/1

Y1 - 2016/10/1

N2 - The H∞ filter problem for a class of fuzzy stochastic discrete neural networks system with mixed delays is studied in this paper. The mixed delays consist of discrete and distributed delays. Based on discrete inequality technic and the Lyapunov–Krasovskii functional approach, sufficient conditions for the existence of admissible filters are established in terms of linear matrix inequalities, which ensure the asymptotical mean-square stability as well as a prescribed H∞ disturbance attenuation level. Examples and simulations are provided to illustrate the effectiveness of the proposed method.

AB - The H∞ filter problem for a class of fuzzy stochastic discrete neural networks system with mixed delays is studied in this paper. The mixed delays consist of discrete and distributed delays. Based on discrete inequality technic and the Lyapunov–Krasovskii functional approach, sufficient conditions for the existence of admissible filters are established in terms of linear matrix inequalities, which ensure the asymptotical mean-square stability as well as a prescribed H∞ disturbance attenuation level. Examples and simulations are provided to illustrate the effectiveness of the proposed method.

KW - Asymptotically mean square stable

KW - Fuzzy systems

KW - H filtering

KW - Linear matrix inequality(LMI)

KW - Stochastic discrete neural networks

UR - https://www.scopus.com/pages/publications/84941335041

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M3 - 文章

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JO - Journal of Applied Mathematics and Computing

JF - Journal of Applied Mathematics and Computing

IS - 1-2

ER -

H_∞ filtering for discrete-time fuzzy stochastic neural networks with mixed time-delays

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Keywords

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