Further results on L2-L∞ state estimation of delayed neural networks
Further results on L2-L∞ state estimation of delayed neural networks
复制标题
DOI：
10.1016/j.neucom.2017.08.027
复制
发表时间：
2018-01
期刊：
影响因子：
6
通讯作者：
W. Qian;Yonggang Chen;Yurong Liu;F. Alsaadi
中科院分区：
文献类型：
--
作者：
W. Qian;Yonggang Chen;Yurong Liu;F. Alsaadi
研究了一类时滞神经网络的L2-L∞状态估计问题。重点讨论了全阶状态估计器的设计，使系统满足L2-L∞性能约束。本文首次提出了一种充分利用时滞信息的L2-L∞性能分析方法。在此基础上，以线性矩阵不等式的形式给出了估计误差动态特性达到L2-L∞性能水平的保守性较弱的充分条件.几个数值例子表明，本文所提出的方法是显着有效地减少可能的保守性。
This paper investigates theL2–L∞state estimation problem for a class of delayed neural networks. Attention is focused on the design of a full-order state estimator such that the prescribedL2–L∞performance constraint can be ensured. By utilizing the time-delay information sufficiently, a novelL2–L∞performance analysis approach is proposed in this paper for the first time. Based on such an approach, the less conservative sufficient conditions are established in terms of linear matrix inequalities under which theL2–L∞performance level can be achieved for the estimation error dynamics. Several numerical examples show that the proposed approach in this paper is explicitly effective in reducing the possible conservatism.