College of Information Science and Technology, Donghua University, Shanghai 201620, China; Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai 201620, China.
College of Information Science and Technology, Donghua University, Shanghai 201620, China; Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai 201620, China.
Neural Netw. 2020 Jan;121:356-365. doi: 10.1016/j.neunet.2019.09.006. Epub 2019 Sep 21.
In this paper, the finite-time resilient H state estimation problem is investigated for a class of discrete-time delayed neural networks. For the sake of energy saving, a dynamic event-triggered mechanism is employed in the design of state estimator for the discrete-time delayed neural networks. In order to handle the possible fluctuation of the estimator gain parameters when the state estimator is implemented, a resilient state estimator is adopted. By constructing a Lyapunov-Krasovskii functional, a sufficient condition is established, which guarantees that the estimation error system is bounded and the H performance requirement is satisfied within the finite time. Then, the desired estimator gains are obtained via solving a set of linear matrix inequalities. Finally, a numerical example is employed to illustrate the usefulness of the proposed state estimation scheme.
本文研究了一类离散时间时滞神经网络的有限时间弹性 H 状态估计问题。为了节能，在离散时间时滞神经网络状态估计器的设计中采用了动态事件触发机制。为了处理状态估计器实施时估计器增益参数可能出现的波动，采用了弹性状态估计器。通过构造李雅普诺夫-克拉索夫斯基泛函，建立了一个充分条件，保证了估计误差系统在有限时间内有界，并且满足 H 性能要求。然后，通过求解一组线性矩阵不等式得到了期望的估计器增益。最后，通过一个数值例子说明了所提出的状态估计方案的有效性。
