Extended Dissipative State Estimation for Markov Jump Neural Networks With Unreliable Links
Extended Dissipative State Estimation for Markov Jump Neural Networks With Unreliable Links
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DOI：
10.1109/tnnls.2015.2511196
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发表时间：
2017-02
影响因子：
10.4
通讯作者：
Hao Shen;Yanzheng Zhu;Lixian Zhang;Ju H. Park
中科院分区：
文献类型：
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作者：
Hao Shen;Yanzheng Zhu;Lixian Zhang;Ju H. Park
研究离散马尔可夫跳变神经网络基于扩展耗散的状态估计问题，其中马尔可夫链的分段时变转移概率的变化取决于一组满足平均停留时间性质的切换信号。假设神经网络与估计器之间的通信链路是不完美的，其中信号量化和数据包丢失现象同时发生。本文的目的是提供一种马尔可夫交换估计器设计方法，该方法可以保证在同时存在由不可靠通信链路引起的丢包和信号量化的情况下，得到的误差系统是扩展随机耗散的。建立了该问题可解的充分条件。在此基础上，给出了期望马尔可夫切换估计量的显式表达式。最后，通过两个算例验证了所提设计方法的有效性。
This paper is concerned with the problem of extended dissipativity-based state estimation for discrete-time Markov jump neural networks (NNs), where the variation of the piecewise time-varying transition probabilities of Markov chain is subject to a set of switching signals satisfying an average dwell-time property. The communication links between the NNs and the estimator are assumed to be imperfect, where the phenomena of signal quantization and data packet dropouts occur simultaneously. The aim of this paper is to contribute with a Markov switching estimator design method, which ensures that the resulting error system is extended stochastically dissipative, in the simultaneous presences of packet dropouts and signal quantization stemmed from unreliable communication links. Sufficient conditions for the solvability of such a problem are established. Based on the derived conditions, an explicit expression of the desired Markov switching estimator is presented. Finally, two illustrated examples are given to show the effectiveness of the proposed design method.