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.

► A new learning law for Takagi–Sugeno fuzzy neural networks is proposed. ► This learning law guarantees passivity and asymptotical stability. ► A new condition for input-to-state stability is proposed under this learning law.