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Entropy 2018, 20(1), 5; https://doi.org/10.3390/e20010005

State Estimation for General Complex Dynamical Networks with Incompletely Measured Information

College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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Received: 16 November 2017 / Revised: 17 December 2017 / Accepted: 20 December 2017 / Published: 23 December 2017
(This article belongs to the Special Issue Research Frontier in Chaos Theory and Complex Networks)
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Abstract

Estimating uncertain state variables of a general complex dynamical network with randomly incomplete measurements of transmitted output variables is investigated in this paper. The incomplete measurements, occurring randomly through the transmission of output variables, always cause the failure of the state estimation process. Different from the existing methods, we propose a novel method to handle the incomplete measurements, which can perform well to balance the excessively deviated estimators under the influence of incomplete measurements. In particular, the proposed method has no special limitation on the node dynamics compared with many existing methods. By employing the Lyapunov stability theory along with the stochastic analysis method, sufficient criteria are deduced rigorously to ensure obtaining the proper estimator gains with known model parameters. Illustrative simulation for the complex dynamical network composed of chaotic nodes are given to show the validity and efficiency of the proposed method. View Full-Text
Keywords: state estimation; complex dynamical network; incomplete measurements state estimation; complex dynamical network; incomplete measurements
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Wang, X.; Jiang, G.-P.; Wu, X. State Estimation for General Complex Dynamical Networks with Incompletely Measured Information. Entropy 2018, 20, 5.

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