A New Result on $H_{\infty }$ State Estimation of Delayed Static Neural Networks
Yueying Wang, Yuanqing Xia, Pingfang Zhou, Dengping Duan · IEEE Transactions on Neural Networks and Learning Systems · 2016
This brief presents a new guaranteed performance state estimation criterion for delayed static neural networks. To facilitate the use of the slope information about activation function, the estimation error of activation function is separated into two parts for the first time. Then, a novel Lyapunov-Krasovskii functional (LKF) is constructed, which has fully captured the slope information of the activation. Based on the new LKF, a less conservative design criterion of estimator is derived to ensure the asymptotic stability of estimation error system with performance. The desired estimator gain matrices and the performance index are obtained by solving a convex optimization problem. The simulation results show that the proposed method has much better performance than the most recent results.