Some new results on stability of Takagi–Sugeno fuzzy Hopfield neural networks

In this paper, we propose some new results on stability properties of Takagi–Sugeno fuzzy Hopfield neural networks with time-delay. Based on Lyapunov stability theory, a new learning law is derived to guarantee passivity and asymptotical stability of Takagi–Sugeno fuzzy Hopfield neural networks. Furthermore, a new condition for input-to-state stability (ISS) is established. Illustrative examples are given to demonstrate the effectiveness of the proposed results.