Robust stability for uncertain delayed fuzzy Hopfield neural networks with Markovian jumping parameters

Hongyi Li; B. Chen; Z. Qi; Q. Weiyi

doi:10.1109/TSMCB.2008.2002812

Robust stability for uncertain delayed fuzzy Hopfield neural networks with Markovian jumping parameters

Hongyi Li
, B. Chen
, Z. Qi
, Q. Weiyi

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

Abstract

This paper is concerned with the problem of the robust stability of nonlinear delayed Hopfield neural networks (HNNs) with Markovian jumping parameters by Takagi-Sugeno (T-S) fuzzy model. The nonlinear delayed HNNs are first established as a modified T-S fuzzy model in which the consequent parts are composed of a set of Markovian jumping HNNs with interval delays. Time delays here are assumed to be time-varying and belong to the given intervals. Based on Lyapunov-Krasovskii stability theory and linear matrix inequality approach, stability conditions are proposed in terms of the upper and lower bounds of the delays. Finally, numerical examples are used to illustrate the effectiveness of the proposed method.

Original language	English
Pages (from-to)	94-102
Number of pages	9
Journal	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume	39
Issue number	1
DOIs	https://doi.org/10.1109/TSMCB.2008.2002812
Publication status	Published - Feb 2009

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title = "Robust stability for uncertain delayed fuzzy Hopfield neural networks with Markovian jumping parameters",

abstract = "This paper is concerned with the problem of the robust stability of nonlinear delayed Hopfield neural networks (HNNs) with Markovian jumping parameters by Takagi-Sugeno (T-S) fuzzy model. The nonlinear delayed HNNs are first established as a modified T-S fuzzy model in which the consequent parts are composed of a set of Markovian jumping HNNs with interval delays. Time delays here are assumed to be time-varying and belong to the given intervals. Based on Lyapunov-Krasovskii stability theory and linear matrix inequality approach, stability conditions are proposed in terms of the upper and lower bounds of the delays. Finally, numerical examples are used to illustrate the effectiveness of the proposed method.",

author = "Hongyi Li and B. Chen and Z. Qi and Q. Weiyi",

year = "2009",

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doi = "10.1109/TSMCB.2008.2002812",

language = "English",

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T1 - Robust stability for uncertain delayed fuzzy Hopfield neural networks with Markovian jumping parameters

AU - Li, Hongyi

AU - Chen, B.

AU - Qi, Z.

AU - Weiyi, Q.

PY - 2009/2

Y1 - 2009/2

N2 - This paper is concerned with the problem of the robust stability of nonlinear delayed Hopfield neural networks (HNNs) with Markovian jumping parameters by Takagi-Sugeno (T-S) fuzzy model. The nonlinear delayed HNNs are first established as a modified T-S fuzzy model in which the consequent parts are composed of a set of Markovian jumping HNNs with interval delays. Time delays here are assumed to be time-varying and belong to the given intervals. Based on Lyapunov-Krasovskii stability theory and linear matrix inequality approach, stability conditions are proposed in terms of the upper and lower bounds of the delays. Finally, numerical examples are used to illustrate the effectiveness of the proposed method.

AB - This paper is concerned with the problem of the robust stability of nonlinear delayed Hopfield neural networks (HNNs) with Markovian jumping parameters by Takagi-Sugeno (T-S) fuzzy model. The nonlinear delayed HNNs are first established as a modified T-S fuzzy model in which the consequent parts are composed of a set of Markovian jumping HNNs with interval delays. Time delays here are assumed to be time-varying and belong to the given intervals. Based on Lyapunov-Krasovskii stability theory and linear matrix inequality approach, stability conditions are proposed in terms of the upper and lower bounds of the delays. Finally, numerical examples are used to illustrate the effectiveness of the proposed method.

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JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

IS - 1

ER -

Robust stability for uncertain delayed fuzzy Hopfield neural networks with Markovian jumping parameters

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