Variance-Constrained Recursive State Estimation for Time-Varying Complex Networks With Quantized Measurements and Uncertain Inner Coupling

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,...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 31; no. 6; pp. 1955 - 1967
Main Authors: Hu, Jun, Wang, Zidong, Liu, Guo-Ping, Zhang, Hongxu
Format: Journal Article
Language:English
Published: United States IEEE 01.06.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Boundedness analysis
Complex networks
Computer simulation
Constraints
Coupling
Couplings
Covariance
Error analysis
Estimation error
Measurement
Measurement uncertainty
optimal state estimation
Quantization (signal)
signal quantization
State estimation
Stochasticity
time-varying stochastic complex networks
uncertain inner coupling
Upper bounds
Variance
variance-constrained approach
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary: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.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2019.2927554