A quadratic convex combination approach on robust dissipativity and passivity analysis for Takagi-Sugeno fuzzy Cohen-Grossberg neural networks with time-varying delays

In this paper, the robust dissipativity and passivity criteria for Takagi-Sugeno fuzzy Cohen-Grossberg neural networks with time-varying delays have been investigated. The delay is of the time-varying nature, and the activation functions are assumed to be neither differentiable nor strictly monotonic. Furthermore, the description of the activation functions is more general than the commonly used Lipschitz conditions. By using a Lyapunov-Krasovskii functional and employing the quadratic convex combination approach, a set of sufficient conditions are established to ensure the dissipativity of the proposed model. The obtained conditions are presented in terms of linear matrix inequalities, so that its feasibility can be checked easily via standard numerical toolboxes. The quadratic convex combination approach used in our paper gives a reduced conservatism without using Jensen's inequality. In addition to that, numerical examples with simulation results are given to show the effectiveness of the obtained linear matrix inequality conditions. Copyright (C) 2016 John Wiley & Sons, Ltd.