New stability results for Takagi–Sugeno fuzzy Cohen–Grossberg neural networks with multiple delays

Autor: Selcuk Sevgen
Rok vydání: 2019
Předmět:
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