State Estimation for Discrete-Time Complex Networks With Time-Varying Outer Coupling and Uncertain Inner Coupling: A Distributed Method

A distributed filtering issue is considered for discrete-time complex networks (CNs) with variable outer coupling and uncertain inner coupling. The outer coupling is described by a matrix with time-varying configuration parameters, and the inner coupling parameters are permitted to change within specific limits. The focus of this study is the design of filters for each node utilizing data from local and neighboring nodes. We prove the estimation error covariance (EEC) is exponentially bounded on the mean square, obtaining an optimal filter gain such that the bound is minimized. And we quantitatively analyzed the relationship between coupling parameters and filtering performance. A series of numerical simulations are given with comparisons to illustrate the filtering performance.

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Publication Details

Journal
IEEE Transactions on Signal and Information Processing over Networks
Published
2025-01-01
DOI
https://doi.org/10.1109/tsipn.2025.3578778
Primary Topic
Network Time Synchronization Technologies
Type
article
Field-Weighted Citation Impact
0.00

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article

State Estimation for Discrete-Time Complex Networks With Time-Varying Outer Coupling and Uncertain Inner Coupling: A Distributed Method

Chulin Zhou, Shiyou Chen, Hao Liu
IEEE Transactions on Signal and Information Processing over Networks
Network Time Synchronization Technologies
article

State Estimation for Discrete-Time Complex Networks With Time-Varying Outer Coupling and Uncertain Inner Coupling: A Distributed Method

Chulin Zhou, Shiyou Chen, Hao Liu
article en

Abstract

A distributed filtering issue is considered for discrete-time complex networks (CNs) with variable outer coupling and uncertain inner coupling. The outer coupling is described by a matrix with time-varying configuration parameters, and the inner coupling parameters are permitted to change within specific limits. The focus of this study is the design of filters for each node utilizing data from local and neighboring nodes. We prove the estimation error covariance (EEC) is exponentially bounded on the mean square, obtaining an optimal filter gain such that the bound is minimized. And we quantitatively analyzed the relationship between coupling parameters and filtering performance. A series of numerical simulations are given with comparisons to illustrate the filtering performance.

IEEE Transactions on Signal and Information Processing over NetworksVol. 11
Shanghai Jiao Tong University (CN), Wuhan Institute of Technology (CN)
National Natural Science Foundation of China, Natural Science Foundation of Shandong Province
Openalex Percentile: Top 86%
Network Time Synchronization Technologies
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