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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Variance-Constrained Recursive State Estimation for Time-Varying Complex Networks With Quantized Measurements and Uncertain Inner Coupling
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Variance-Constrained Recursive State Estimation for Time-Varying Complex Networks With Quantized Measurements and Uncertain Inner Coupling

机译：具有量化测量和不确定内耦合的时变复合网络的变差约束递归状态估计

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摘要

In this paper, a new recursive state estimation problem is discussed for a class of discrete time-varying stochastic complex networks with uncertain inner coupling and signal quantization under the error-variance constraints. The coupling strengths are allowed to be varying within certain intervals, and the measurement signals are subject to the quantization effects before being transmitted to the remote estimator. The focus of the conducted topic is on the design of a variance-constrained state estimation algorithm with the aim to ensure a locally minimized upper bound on the estimation error covariance at every sampling instant. Furthermore, the boundedness of the resulting estimation error is analyzed, and a sufficient criterion is established to ensure the desired exponential boundedness of the state estimation error in the mean square sense. Finally, some simulations are proposed with comparisons to illustrate the validity of the newly developed variance-constrained estimation method.
机译：在本文中，对于具有不确定内耦合的离散时变随机复杂网络以及在误差方差约束下的信号量化讨论了新的递归状态估计问题。允许耦合强度在某些间隔内变化，并且测量信号在被传输到远程估计器之前经受量化效果。所进行主题的焦点是在各方差约束状态估计算法的设计中，目的是确保在每个采样瞬间在估计误差协方差上局部最小化的上限。此外，分析了所产生的估计误差的有界性，并且建立了足够的标准，以确保均线均衡状态估计误差的所需指数偏移。最后，提出了一些模拟，以说明新开发的方差约束估计方法的有效性。

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