H∞ state estimation for discrete-time delayed neural networks with randomly occurring quantizations and missing measurements
H∞ state estimation for discrete-time delayed neural networks with randomly occurring quantizations and missing measurements
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DOI：
10.1016/j.neucom.2014.06.017
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发表时间：
2015-01
期刊：
影响因子：
6
通讯作者：
Jie Zhang;Zidong Wang;Derui Ding;Xiaohui Liu
中科院分区：
文献类型：
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作者：
Jie Zhang;Zidong Wang;Derui Ding;Xiaohui Liu
研究了一类具有时变时滞、随机发生量化（ROQ）和观测缺失的离散神经网络的H∞状态估计问题. ROQ和丢失测量的现象是由伯努利分布的随机序列。所解决的问题的目的是设计一个状态估计器，使得估计误差的动态是指数稳定的均方和指定的H∞性能约束得到满足。通过构造适当的Lyapunov-Krasovskii泛函和采用随机分析技术，充分条件，以确保所需的估计量的存在。此外，所需的估计器的增益的显式表达式中描述的凸优化问题的解决方案，可以很容易地解决，通过使用半定规划方法。最后，一个数值例子来证明所提出的估计器设计方法的有效性和适用性。
This paper is concerned with the H∞ state estimation problem for a class of discrete-time neural networks with time-varying delays, randomly occurring quantizations (ROQs) as well as missing measurements. The phenomena of ROQ and missing measurements are governed by a Bernoulli distributed stochastic sequence. The purpose of the addressed problem is to design a state estimator such that the dynamics of the estimation error is exponentially stable in the mean square and the prescribed H∞ performance constraint is satisfied. By constructing proper Lyapunov–Krasovskii functionals and employing stochastic analysis techniques, sufficient conditions are derived to ensure the existence of the desired estimator. Furthermore, the explicit expression of the gain of the desired estimator is described in terms of the solution to a convex optimization problem that can be easily solved by using the semi-definite programme method. Finally, a numerical example is employed to demonstrate the effectiveness and applicability of the proposed estimator design approach.