Robust exponential stability of uncertain fuzzy Cohen–Grossberg neural networks with time-varying delays

In this paper, the Takagi–Sugeno (T–S) fuzzy model representation is extended to the stability analysis for uncertain Cohen–Grossberg neural networks (CGNNs) with time-varying delays. A novel linear matrix inequality (LMI) based stability criterion is obtained by using Lyapunov functional theory to guarantee the exponential stability of uncertain CGNNs with time varying delays which are represented by T–S fuzzy models. Finally, the proposed stability conditions are demonstrated with numerical examples.