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.
Highlights
The past few decades have witnessed the rapid growth of research interests in the complex dynamical networks
Since the small-world [2] and scale-free [3] network models were proposed, it was possible to explore the deeper behavior in the complex dynamical networks, such as social network [4] and the Internet [5]
We focus on the state estimation of complex dynamical networks considering incomplete measurements
Summary
The past few decades have witnessed the rapid growth of research interests in the complex dynamical networks. A large number of existing studies, concerning the synchronization or other problems of complex networks, have assumed that the state variables transmitted for coupling or communication could be completely measured. In order to apply to more real environments or meet certain engineering requirements, a number of unreliable or uncertain factors have been taken into account on the state estimation problem, such as coupling time delays [20,21], stochastic noisy disturbance [22], uncertain network parameters [23], incomplete measurements [24,25,26,27,28,29], etc.
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