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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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
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Abstract	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.
AbstractList	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.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. 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.
Author	Hu, Jun Liu, Guo-Ping Wang, Zidong Zhang, Hongxu
Author_xml	– sequence: 1 givenname: Jun orcidid: 0000-0002-7852-5064 surname: Hu fullname: Hu, Jun email: hujun2013@gmail.com organization: School of Engineering, University of South Wales, Pontypridd, U.K – sequence: 2 givenname: Zidong orcidid: 0000-0002-9576-7401 surname: Wang fullname: Wang, Zidong email: zidong.wang@brunel.ac.uk organization: Department of Computer Science, Brunel University London, Uxbridge, U.K – sequence: 3 givenname: Guo-Ping orcidid: 0000-0002-0699-2296 surname: Liu fullname: Liu, Guo-Ping email: guoping.liu@southwales.ac.uk organization: School of Engineering, University of South Wales, Pontypridd, U.K – sequence: 4 givenname: Hongxu surname: Zhang fullname: Zhang, Hongxu email: hongxuzhang@hrbust.edu.cn organization: School of Measurement Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin, China
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SubjectTerms	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
Title	Variance-Constrained Recursive State Estimation for Time-Varying Complex Networks With Quantized Measurements and Uncertain Inner Coupling
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