Delay fractioning approach to robust exponential stability of fuzzy Cohen–Grossberg neural networks

In this paper, the problem of robust exponential stability analysis for a class of Takagi–Sugeno (TS) fuzzy Cohen–Grossberg neural networks with uncertainties and time-varying delays is investigated. A generalized activation function is used, and the assumptions such as boundedness, monotony and differentiability of the activation functions are removed. By using a Lyapunov–Krasovskii functional and employing the delay fractioning approach, a set of sufficient conditions are established for achieving the required result. The obtained conditions are proposed in terms of linear matrix inequalities (LMIs), so its feasibility can be checked easily via standard numerical toolboxs. The main advantage of the proposed criteria lies in its reduced conservatism which is mainly based on the time delay fractioning technique. In addition to that, a numerical example with simulation results is given to show the effectiveness of the obtained LMI conditions.