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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Asynchronous Dissipative State Estimation for Stochastic Complex Networks With Quantized Jumping Coupling and Uncertain Measurements
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Asynchronous Dissipative State Estimation for Stochastic Complex Networks With Quantized Jumping Coupling and Uncertain Measurements

机译：带有量化跳变耦合和不确定测量的随机复杂网络的异步耗散状态估计

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摘要

This paper addresses the problem of state estimation for a class of discrete-time stochastic complex networks with a constrained and randomly varying coupling and uncertain measurements. The randomly varying coupling is governed by a Markov chain, and the capacity constraint is handled by introducing a logarithmic quantizer. The uncertainty of measurements is modeled by a multiplicative noise. An asynchronous estimator is designed to overcome the difficulty that each node cannot access to the coupling information, and an augmented estimation error system is obtained using the Kronecker product. Sufficient conditions are established, which guarantee that the estimation error system is stochastically stable and achieves the strict (Q, S, R)-γ-dissipativity. Then, the estimator gains are derived using the linear matrix inequality method. Finally, a numerical example is provided to illustrate the effectiveness of the proposed new design techniques.
机译：本文针对一类具有受限且随机变化的耦合和不确定度量的离散时间随机复杂网络的状态估计问题。随机变化的耦合由马尔可夫链控制，并且通过引入对数量化器来处理容量约束。测量的不确定性由乘性噪声建模。设计异步估计器以克服每个节点无法访问耦合信息的困难，并使用Kronecker产品获得增强的估计误差系统。建立了充分的条件，这保证了估计误差系统是随机稳定的，并实现了严格的（Q，S，R）-γ耗散性。然后，使用线性矩阵不等式方法得出估计器增益。最后，提供了一个数值示例来说明所提出的新设计技术的有效性。

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  • 作者

    Yong Xu; Renquan Lu; Hui Peng; Kan Xie; Anke Xue;

  • 作者单位

    Guangdong Key Laboratory of IOT Information Processing, School of Automation, Guangdong University of Technology, Guangzhou, China;

    Guangdong Key Laboratory of IOT Information Processing, School of Automation, Guangdong University of Technology, Guangzhou, China;

    Key Laboratory for IOT and Information Fusion Technology of Zhejiang, Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, China;

    Guangdong Key Laboratory of IOT Information Processing, School of Automation, Guangdong University of Technology, Guangzhou, China;

    Key Laboratory for IOT and Information Fusion Technology of Zhejiang, Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, China;

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  • 正文语种 eng
  • 中图分类
  • 关键词

    Complex networks; Couplings; Measurement uncertainty; State estimation; Noise measurement; Markov processes;

    机译：复杂网络;耦合;测量不确定度;状态估计;噪声测量;马尔可夫过程;

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