Takagi-Sugeno fuzzy hopfield neural networks for H _∞ nonlinear system identification

In this paper, we propose a new H _∞ weight learning algorithm (HWLA) for nonlinear system identification via Takagi.Sugeno (T.S) fuzzy Hopfield neural networks with time-delay. Based on Lyapunov stability theory, for the first time, the HWLA fornonlinear system identification is presented to reduce the effect of disturbance to an H_∞ norm constraint. The HWLA can be obtained by solving a convex optimization problem which is represented in terms of linear matrix inequality (LMI). An illustrative example is given to demonstrate the effectiveness of the proposed identification scheme.