UKF-based remote state estimation for discrete artificial neural networks with communication bandwidth constraints
This paper is concerned with the remote state estimator design problem for a class of discrete neural networks under communication bandwidth constraints. Due to the limited bandwidth of the transmission channel, only partial components of the measurement outputs can be transmitted to the remote esti...
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| Published in | Neural networks Vol. 108; pp. 393 - 398 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.12.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0893-6080 1879-2782 1879-2782 |
| DOI | 10.1016/j.neunet.2018.08.015 |
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| Abstract | This paper is concerned with the remote state estimator design problem for a class of discrete neural networks under communication bandwidth constraints. Due to the limited bandwidth of the transmission channel, only partial components of the measurement outputs can be transmitted to the remote estimator at each time step. A UKF-based state estimator is developed to cope with the nonlinear activation functions in the neural networks subject to the communication constraints. Moreover, the stability of the proposed estimator is analyzed. Sufficient conditions are established under which the error dynamics of the state estimation is exponentially bounded in mean square. A numerical example is provided to demonstrate the effectiveness of the proposed method. |
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| AbstractList | This paper is concerned with the remote state estimator design problem for a class of discrete neural networks under communication bandwidth constraints. Due to the limited bandwidth of the transmission channel, only partial components of the measurement outputs can be transmitted to the remote estimator at each time step. A UKF-based state estimator is developed to cope with the nonlinear activation functions in the neural networks subject to the communication constraints. Moreover, the stability of the proposed estimator is analyzed. Sufficient conditions are established under which the error dynamics of the state estimation is exponentially bounded in mean square. A numerical example is provided to demonstrate the effectiveness of the proposed method. This paper is concerned with the remote state estimator design problem for a class of discrete neural networks under communication bandwidth constraints. Due to the limited bandwidth of the transmission channel, only partial components of the measurement outputs can be transmitted to the remote estimator at each time step. A UKF-based state estimator is developed to cope with the nonlinear activation functions in the neural networks subject to the communication constraints. Moreover, the stability of the proposed estimator is analyzed. Sufficient conditions are established under which the error dynamics of the state estimation is exponentially bounded in mean square. A numerical example is provided to demonstrate the effectiveness of the proposed method.This paper is concerned with the remote state estimator design problem for a class of discrete neural networks under communication bandwidth constraints. Due to the limited bandwidth of the transmission channel, only partial components of the measurement outputs can be transmitted to the remote estimator at each time step. A UKF-based state estimator is developed to cope with the nonlinear activation functions in the neural networks subject to the communication constraints. Moreover, the stability of the proposed estimator is analyzed. Sufficient conditions are established under which the error dynamics of the state estimation is exponentially bounded in mean square. A numerical example is provided to demonstrate the effectiveness of the proposed method. |
| Author | Zhou, Donghua Liu, Yang Wang, Zidong |
| Author_xml | – sequence: 1 givenname: Yang orcidid: 0000-0003-0253-0358 surname: Liu fullname: Liu, Yang email: lianinliyan@163.com organization: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China – 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, Middlesex, UB8 3PH, United Kingdom – sequence: 3 givenname: Donghua surname: Zhou fullname: Zhou, Donghua email: zdh@mail.tsinghua.edu.cn organization: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30268060$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_neunet_2023_02_028 crossref_primary_10_1080_00207721_2022_2049919 crossref_primary_10_1109_TNNLS_2020_2991151 crossref_primary_10_1016_j_neucom_2019_03_043 crossref_primary_10_1002_rnc_5614 crossref_primary_10_1109_TNNLS_2019_2934131 crossref_primary_10_1109_TNNLS_2022_3201160 crossref_primary_10_1109_ACCESS_2019_2915135 crossref_primary_10_1016_j_isatra_2021_07_012 crossref_primary_10_1080_08839514_2021_1966884 |
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| Keywords | Error boundedness Communication bandwidth constraints Unscented Kalman filtering State estimation Artificial neural networks |
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| SubjectTerms | Algorithms Artificial neural networks Communication Communication bandwidth constraints Computer Simulation Error boundedness Neural Networks (Computer) State estimation Time Factors Unscented Kalman filtering |
| Title | UKF-based remote state estimation for discrete artificial neural networks with communication bandwidth constraints |
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