期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 31, 期 6, 页码 1955-1967出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2927554
关键词
Couplings; Complex networks; Quantization (signal); State estimation; Measurement uncertainty; Estimation error; Boundedness analysis; optimal state estimation; signal quantization; time-varying stochastic complex networks; uncertain inner coupling; variance-constrained approach
类别
资金
- National Natural Science Foundation of China [61673141, 61873148, 61773144]
- Outstanding Youth Science Foundation of Heilongjiang Province of China [JC2018001]
- Fok Ying Tung Education Foundation of China [151004]
- Alexander von Humboldt Foundation of Germany
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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