New stability criteria for Takagi-Sugeno fuzzy Hopfield neural networks

Choon Ki Ahn

New stability criteria for Takagi-Sugeno fuzzy Hopfield neural networks

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

Abstract

In this paper, new stability criteria are derived for Takagi-Sugeno (T-S) fuzzy Hopfield neural networks via the input/output-to-state stability (IOSS) approach. Based on matrix norm and linear matrix inequality (LMI), these stability criteria guarantee input/output-to-state stability for external input vector. Moreover, the criteria for asymptotic stability of T-S fuzzy Hopfield neural networks without external input vector are presented.

Original language	English
Pages (from-to)	237-246
Number of pages	10
Journal	International Journal of Pure and Applied Mathematics
Volume	75
Issue number	2
Publication status	Published - 2012
Externally published	Yes

Keywords

Input/output-to-state stability (IOSS)
Linear matrix inequality (LMI)
Takagi-Sugeno (T-S) fuzzy Hopfield neural networks

ASJC Scopus subject areas

Mathematics(all)
Applied Mathematics

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AB - In this paper, new stability criteria are derived for Takagi-Sugeno (T-S) fuzzy Hopfield neural networks via the input/output-to-state stability (IOSS) approach. Based on matrix norm and linear matrix inequality (LMI), these stability criteria guarantee input/output-to-state stability for external input vector. Moreover, the criteria for asymptotic stability of T-S fuzzy Hopfield neural networks without external input vector are presented.

KW - Input/output-to-state stability (IOSS)

KW - Linear matrix inequality (LMI)

KW - Takagi-Sugeno (T-S) fuzzy Hopfield neural networks

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ER -

New stability criteria for Takagi-Sugeno fuzzy Hopfield neural networks

Abstract

Keywords

ASJC Scopus subject areas

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