H∞ State Estimation for Takagi-Sugeno Fuzzy Delayed Hopfield Neural Networks
Choon Ki Ahn · International Journal of Computational Intelligence Systems · 2011
This paper presents a new H ∞ state estimator for Takagi-Sugeno fuzzy delayed Hopfield neural networks. Based on Lyapunov-Krasovskii stability approach, a delay-dependent criterion is proposed to ensure that the resulting estimation error system is asymptotically stable with a guaranteed H ∞ performance. The proposed H ∞ state estimator can be realized by solving a linear matrix inequality (LMI) problem. An illustrative numerical example is given to verify the effectiveness of the proposed H ∞ state estimator.