Takagi–Sugeno Fuzzy Hopfield Neural Networks for \({\mathcal{H}_{\infty}}\) Nonlinear System Identification

摘要

In this paper, we propose a new \({\mathcal H_\infty}\) 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 \({\mathcal{H}_{\infty }}\) 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.