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

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 non-singular 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. (c) 2019 Elsevier Ltd. All rights reserved.