Fuzzy logic derivation of neural network models with time delays in subsystems

C.Y. Chen; John Hsu; C.W. Chen

doi:10.1142/S021821300500248X

Fuzzy logic derivation of neural network models with time delays in subsystems

C.Y. Chen, John Hsu, C.W. Chen

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

74 Citations (Web of Science)

Abstract

This paper extends the Takagi-Sugeno (T-S) fuzzy model representation to analyze the stability of interconnected systems in which there exist time delays in subsystems. A novel stability criterion which can be solved numerically is presented in terms of Lyapunov's theory for fuzzy interconnected models. In this paper, we use linear difference inclusion (LDI) state-space representation to represent the fuzzy model. Then, the linear matrix inequality (LMI) optimization algorithm is employed to find common solution and then guarantee the asymptotic stability.

Original language	English
Pages (from-to)	967-974
Journal	International Journal on Artificial Intelligence Tools
Volume	14
Issue number	6
DOIs	https://doi.org/10.1142/S021821300500248X
Publication status	Published - 2005

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title = "Fuzzy logic derivation of neural network models with time delays in subsystems",

abstract = "This paper extends the Takagi-Sugeno (T-S) fuzzy model representation to analyze the stability of interconnected systems in which there exist time delays in subsystems. A novel stability criterion which can be solved numerically is presented in terms of Lyapunov's theory for fuzzy interconnected models. In this paper, we use linear difference inclusion (LDI) state-space representation to represent the fuzzy model. Then, the linear matrix inequality (LMI) optimization algorithm is employed to find common solution and then guarantee the asymptotic stability.",

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AU - Hsu, John

AU - Chen, C.W.

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N2 - This paper extends the Takagi-Sugeno (T-S) fuzzy model representation to analyze the stability of interconnected systems in which there exist time delays in subsystems. A novel stability criterion which can be solved numerically is presented in terms of Lyapunov's theory for fuzzy interconnected models. In this paper, we use linear difference inclusion (LDI) state-space representation to represent the fuzzy model. Then, the linear matrix inequality (LMI) optimization algorithm is employed to find common solution and then guarantee the asymptotic stability.

AB - This paper extends the Takagi-Sugeno (T-S) fuzzy model representation to analyze the stability of interconnected systems in which there exist time delays in subsystems. A novel stability criterion which can be solved numerically is presented in terms of Lyapunov's theory for fuzzy interconnected models. In this paper, we use linear difference inclusion (LDI) state-space representation to represent the fuzzy model. Then, the linear matrix inequality (LMI) optimization algorithm is employed to find common solution and then guarantee the asymptotic stability.

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DO - 10.1142/S021821300500248X

M3 - Article

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VL - 14

SP - 967

EP - 974

JO - International Journal on Artificial Intelligence Tools

JF - International Journal on Artificial Intelligence Tools

IS - 6

ER -

Fuzzy logic derivation of neural network models with time delays in subsystems

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