Some new results on stability of Takagi-Sugeno fuzzy Hopfield neural networks

Choon Ki Ahn

doi:10.1016/j.fss.2011.05.010

Some new results on stability of Takagi-Sugeno fuzzy Hopfield neural networks

Choon Ki Ahn

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

45 Citations (Scopus)

Abstract

In this paper, we propose some new results on stability properties of Takagi-Sugeno fuzzy Hopfield neural networks with time-delay. Based on Lyapunov stability theory, a new learning law is derived to guarantee passivity and asymptotical stability of Takagi-Sugeno fuzzy Hopfield neural networks. Furthermore, a new condition for input-to-state stability (ISS) is established. Illustrative examples are given to demonstrate the effectiveness of the proposed results.

Original language	English
Pages (from-to)	100-111
Number of pages	12
Journal	Fuzzy Sets and Systems
Volume	179
Issue number	1
DOIs	https://doi.org/10.1016/j.fss.2011.05.010
Publication status	Published - 2011 Sept 16
Externally published	Yes

Keywords

Input-to-state stability (ISS)
Learning
Lyapunov stability theory
Neuro-fuzzy systems
Passivity

ASJC Scopus subject areas

Logic
Artificial Intelligence

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10.1016/j.fss.2011.05.010

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abstract = "In this paper, we propose some new results on stability properties of Takagi-Sugeno fuzzy Hopfield neural networks with time-delay. Based on Lyapunov stability theory, a new learning law is derived to guarantee passivity and asymptotical stability of Takagi-Sugeno fuzzy Hopfield neural networks. Furthermore, a new condition for input-to-state stability (ISS) is established. Illustrative examples are given to demonstrate the effectiveness of the proposed results.",

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AB - In this paper, we propose some new results on stability properties of Takagi-Sugeno fuzzy Hopfield neural networks with time-delay. Based on Lyapunov stability theory, a new learning law is derived to guarantee passivity and asymptotical stability of Takagi-Sugeno fuzzy Hopfield neural networks. Furthermore, a new condition for input-to-state stability (ISS) is established. Illustrative examples are given to demonstrate the effectiveness of the proposed results.

KW - Input-to-state stability (ISS)

KW - Learning

KW - Lyapunov stability theory

KW - Neuro-fuzzy systems

KW - Passivity

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Some new results on stability of Takagi-Sugeno fuzzy Hopfield neural networks

Abstract

Keywords

ASJC Scopus subject areas

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