New stability results for Takagi–Sugeno fuzzy Cohen–Grossberg neural networks with multiple delays
Autor: | Selcuk Sevgen |
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Rok vydání: | 2019 |
Předmět: |
Lyapunov stability
0209 industrial biotechnology Time Factors Mathematics::Commutative Algebra Artificial neural network Computer science Cognitive Neuroscience Stability (learning theory) 02 engineering and technology Fuzzy control system Computer Science::Artificial Intelligence Lipschitz continuity Fuzzy logic Nonlinear system 020901 industrial engineering & automation Exponential stability Computer Science::Systems and Control Artificial Intelligence Control theory 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Neural Networks Computer Algorithms |
Zdroj: | Neural Networks. 114:60-66 |
ISSN: | 0893-6080 |
DOI: | 10.1016/j.neunet.2019.02.010 |
Popis: | This work focuses on global asymptotic stability of Takagi–Sugeno fuzzy Cohen–Grossberg neural networks with multiple time delays. By using the standard Lyapunov stability techniques and nonsingular M-matrix condition of matrices together with employing the nonlinear Lipschitz activation functions, a new easily verifiable sufficient criterion is obtained to guarantee global asymptotic stability of the Cohen–Grossberg neural network model which is represented by a Takagi–Sugeno fuzzy model. A constructive numerical example is studied to demonstrate the effectiveness of the proposed theoretical results. This numerical example is also used to make a comparison between the global stability condition obtained in this study and some of previously published global stability results. This comparison reveals that the condition we propose establishes a novel and alternative stability result for Takagi–Sugeno fuzzy Cohen–Grossberg neural networks of this class. |
Databáze: | OpenAIRE |
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