Takagi-Sugeno Fuzzy Hopfield Neural Networks for H-infinity Nonlinear System Identification
In this paper, we propose a new H-infinity 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 for nonlinear system identification is presented to reduce the effect of disturbance to an H-infinity 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.
takagi-sugeno fuzzy hopfield neural networks h-infinity nonlinear system identification weight learning algorithm lyapunov stability theory linear matrix inequality (lmi)