IEEE Trans Neural Netw Learn Syst. 2017 Feb;28(2):268-277. doi: 10.1109/TNNLS.2015.2503772. Epub 2015 Dec 24.
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.
本文针对一类具有约束和随机时变耦合以及不确定测量的离散时间随机复杂网络的状态估计问题进行了研究。随机时变耦合由马尔可夫链控制，容量约束通过引入对数量化器来处理。测量的不确定性通过乘性噪声进行建模。设计了一种异步估计器来克服每个节点无法访问耦合信息的困难，并使用克罗内克积获得了扩展的估计误差系统。建立了充分条件，保证了估计误差系统的随机稳定性，并实现了严格（Q，S，R）-γ耗散性。然后，使用线性矩阵不等式方法推导出了估计器增益。最后，通过数值示例验证了所提出的新设计技术的有效性。
