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,...

Full description

Saved in:

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
Tags:	Add Tag No Tags, Be the first to tag this record!

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
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31395561$$D View this record in MEDLINE/PubMed
BookMark	eNp9kc1uEzEUhS1UREvpC4CELLFhM8E_Y3tmiaIClUIQNAV2I8e-Ay4zdrA9QHkEnhqnCVl0gTfXi--cc3XPQ3TkgweEHlMyo5S0L1bL5eJyxghtZ6xlSoj6HjphVLKK8aY5OvzV52N0ltI1KU8SIev2ATrmlLdCSHqC_nzU0WlvoJoHn3LUzoPFH8BMMbkfgC-zzoDPU3ajzi543IeIV26EqghvnP-C52HcDPALLyH_DPFbwp9c_orfT9pn97t4vQWdpggj-Jyw9hZflbSYSxC-8B5iMZg2Q3F6hO73ekhwtp-n6OrV-Wr-plq8e30xf7moTFk6V5avJZUCFGholWwsF6q3hlFhNdOKrpmQillWa2YV4RbMGrjsNQUpWF0bfoqe73w3MXyfIOVudMnAMGgPYUodY4oQKolUBX12B70OU_Rlu47VpOWqFZIX6umemtYj2G4Ty7HiTffvygVgO8DEkFKE_oBQ0m3b7G7b7LZtdvs2i6i5IzIu33awbWn4v_TJTuoA4JDVqKauacv_AonHrrA
CODEN	ITNNAL
CitedBy_id	crossref_primary_10_1109_TCNS_2020_3035759 crossref_primary_10_1016_j_jfranklin_2024_106838 crossref_primary_10_1080_00207721_2020_1817616 crossref_primary_10_1016_j_neunet_2020_08_023 crossref_primary_10_1016_j_neucom_2021_03_022 crossref_primary_10_1002_acs_3803 crossref_primary_10_1109_TASE_2024_3398731 crossref_primary_10_1109_TNNLS_2020_3027252 crossref_primary_10_1007_s00034_020_01378_x crossref_primary_10_1109_TCYB_2020_3036364 crossref_primary_10_1109_TSIPN_2025_3578778 crossref_primary_10_1109_TCNS_2024_3488515 crossref_primary_10_1007_s00034_019_01327_3 crossref_primary_10_1109_TNSE_2022_3163258 crossref_primary_10_1007_s12555_020_0051_3 crossref_primary_10_1016_j_neucom_2020_01_092 crossref_primary_10_1109_TSMC_2022_3218532 crossref_primary_10_1109_TCYB_2021_3049461 crossref_primary_10_1109_TNNLS_2021_3131661 crossref_primary_10_1109_TNNLS_2023_3302190 crossref_primary_10_1109_JSYST_2022_3142183 crossref_primary_10_1002_rnc_5801 crossref_primary_10_1016_j_jfranklin_2022_05_009 crossref_primary_10_1002_rnc_7669 crossref_primary_10_1007_s12555_020_0518_2 crossref_primary_10_1016_j_neucom_2019_12_064 crossref_primary_10_1109_TAC_2024_3378776 crossref_primary_10_1016_j_neucom_2020_03_015 crossref_primary_10_1016_j_jfranklin_2023_10_019 crossref_primary_10_1002_rnc_6581 crossref_primary_10_1016_j_sysconle_2023_105692 crossref_primary_10_1109_ACCESS_2023_3341425 crossref_primary_10_1109_TCSI_2021_3071034 crossref_primary_10_1109_TCSI_2024_3372548 crossref_primary_10_1007_s11432_023_3905_1 crossref_primary_10_1080_21642583_2020_1740113 crossref_primary_10_1049_cth2_12049 crossref_primary_10_1016_j_ins_2020_08_047 crossref_primary_10_1016_j_jfranklin_2025_107988 crossref_primary_10_1016_j_neucom_2021_03_086 crossref_primary_10_1080_21642583_2020_1746210 crossref_primary_10_1109_TCYB_2021_3057545 crossref_primary_10_1109_ACCESS_2021_3118338 crossref_primary_10_1016_j_automatica_2021_109961 crossref_primary_10_1016_j_neucom_2019_12_038 crossref_primary_10_1080_00207721_2020_1856447 crossref_primary_10_1080_21642583_2020_1734986 crossref_primary_10_1016_j_compbiomed_2023_107901 crossref_primary_10_1016_j_automatica_2025_112291 crossref_primary_10_1109_TCYB_2020_3043283 crossref_primary_10_1002_asjc_2906 crossref_primary_10_1016_j_neunet_2022_02_028 crossref_primary_10_1109_TNNLS_2021_3102127 crossref_primary_10_1109_TAC_2021_3091182 crossref_primary_10_1109_TASE_2025_3577977 crossref_primary_10_1109_TCYB_2022_3222628 crossref_primary_10_1109_TSMC_2021_3071390 crossref_primary_10_1109_TAC_2023_3349102 crossref_primary_10_1109_TCYB_2020_3025251 crossref_primary_10_1080_00207721_2022_2049919 crossref_primary_10_1016_j_isatra_2025_04_033 crossref_primary_10_1007_s12555_019_1000_x crossref_primary_10_1016_j_cnsns_2025_109294 crossref_primary_10_1080_21642583_2021_1997670 crossref_primary_10_1016_j_neucom_2021_01_023 crossref_primary_10_1016_j_neucom_2022_08_054 crossref_primary_10_1016_j_automatica_2022_110628 crossref_primary_10_1080_21642583_2020_1737846 crossref_primary_10_1007_s11063_023_11430_x crossref_primary_10_1080_21642583_2022_2086183 crossref_primary_10_1007_s12555_020_0485_7 crossref_primary_10_1109_TNSE_2021_3076113 crossref_primary_10_1109_TFUZZ_2022_3193446 crossref_primary_10_1007_s40747_024_01728_1 crossref_primary_10_1109_TNNLS_2019_2953649 crossref_primary_10_1080_00207721_2021_1917721 crossref_primary_10_1016_j_neucom_2020_03_104 crossref_primary_10_1080_00207721_2021_1885082 crossref_primary_10_1016_j_automatica_2021_109784 crossref_primary_10_1016_j_automatica_2021_109782 crossref_primary_10_1177_01423312251358173 crossref_primary_10_1007_s11768_023_00164_9 crossref_primary_10_1016_j_inffus_2021_06_006 crossref_primary_10_1007_s12555_020_0091_8 crossref_primary_10_1109_TNNLS_2021_3106947 crossref_primary_10_1109_TCYB_2020_3021194 crossref_primary_10_1016_j_amc_2024_129126 crossref_primary_10_1080_00207721_2020_1868615 crossref_primary_10_1002_rnc_5328 crossref_primary_10_1016_j_neucom_2020_01_066 crossref_primary_10_1016_j_neucom_2020_01_065 crossref_primary_10_1080_21642583_2021_1907259 crossref_primary_10_1109_TFUZZ_2023_3315722 crossref_primary_10_1177_09596518221132364 crossref_primary_10_1109_TSP_2020_3048245 crossref_primary_10_1016_j_isatra_2021_12_036 crossref_primary_10_1109_TNNLS_2020_3027467 crossref_primary_10_1109_TCYB_2020_3021982 crossref_primary_10_1007_s13042_021_01285_w crossref_primary_10_1109_TSMC_2020_2966977 crossref_primary_10_1016_j_neucom_2019_09_053 crossref_primary_10_1109_JSYST_2024_3357901 crossref_primary_10_1016_j_neunet_2020_08_006 crossref_primary_10_1109_TCYB_2020_2981646 crossref_primary_10_1109_TNNLS_2024_3474016 crossref_primary_10_1002_oca_2987 crossref_primary_10_1016_j_jfranklin_2020_04_012 crossref_primary_10_1109_TNNLS_2021_3070797 crossref_primary_10_1016_j_cnsns_2024_108422 crossref_primary_10_1109_ACCESS_2019_2956287 crossref_primary_10_1007_s12555_018_0780_8 crossref_primary_10_3390_s25092880 crossref_primary_10_1007_s00034_022_02059_7 crossref_primary_10_1016_j_amc_2021_125960 crossref_primary_10_1002_rnc_5368 crossref_primary_10_1080_23335777_2020_1837249 crossref_primary_10_1109_TSMC_2021_3049306 crossref_primary_10_1109_JSYST_2021_3064920 crossref_primary_10_1016_j_nahs_2023_101365 crossref_primary_10_1186_s13662_020_02896_3 crossref_primary_10_1049_iet_cta_2020_0219 crossref_primary_10_1002_mma_10286 crossref_primary_10_1080_21642583_2021_1888820 crossref_primary_10_1109_TNSE_2025_3567517 crossref_primary_10_1109_TNSE_2021_3058220 crossref_primary_10_1016_j_jfranklin_2022_05_026 crossref_primary_10_1155_2019_7121652 crossref_primary_10_1109_TNSE_2021_3095217
Cites_doi	10.1109/TIE.2015.2475515 10.1038/srep26733 10.1109/TNNLS.2015.2503772 10.1049/iet-cta.2016.1161 10.1109/TNNLS.2013.2271357 10.1016/j.physa.2012.07.007 10.1109/TNNLS.2014.2387443 10.1109/9.754809 10.4173/mic.2009.1.3 10.1016/j.amc.2017.02.041 10.1016/j.inffus.2017.03.003 10.1109/TCYB.2016.2568281 10.1016/j.amc.2013.10.068 10.1016/j.neucom.2018.07.086 10.1137/060657935 10.1109/TCYB.2015.2399334 10.1016/j.automatica.2018.07.027 10.1016/0167-6911(92)90097-C 10.1016/j.amc.2017.05.007 10.1109/TNNLS.2013.2253122 10.1109/TIE.2017.2701776 10.1109/TFUZZ.2014.2350534 10.1016/j.physrep.2005.10.009 10.1016/j.jfranklin.2016.08.021 10.1109/TSP.2017.2686375 10.1109/TNNLS.2012.2202246 10.1109/TCYB.2015.2509170 10.1109/TCYB.2016.2536748 10.1016/j.neucom.2018.08.060 10.1080/00207721.2012.670309 10.1080/03081079.2014.973725 10.1109/TNN.2010.2090669 10.1109/TCYB.2014.2386781 10.1109/TAC.2005.858689 10.1109/TCYB.2017.2729164 10.1109/TCYB.2017.2653242 10.1109/TNNLS.2017.2678681 10.1109/TIE.2016.2587246 10.1016/j.inffus.2016.06.008 10.1109/TMECH.2017.2700459 10.1016/j.automatica.2015.11.008
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020
DBID	97E RIA RIE AAYXX CITATION NPM 7QF 7QO 7QP 7QQ 7QR 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8
DOI	10.1109/TNNLS.2019.2927554
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Xplore CrossRef PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Calcium & Calcified Tissue Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Neurosciences Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts MEDLINE - Academic
DatabaseTitle	CrossRef PubMed Materials Research Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Materials Business File Aerospace Database Engineered Materials Abstracts Biotechnology Research Abstracts Chemoreception Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Civil Engineering Abstracts Aluminium Industry Abstracts Electronics & Communications Abstracts Ceramic Abstracts Neurosciences Abstracts METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Solid State and Superconductivity Abstracts Engineering Research Database Calcium & Calcified Tissue Abstracts Corrosion Abstracts MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic Materials Research Database PubMed
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Xplore url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	2162-2388
EndPage	1967
ExternalDocumentID	31395561 10_1109_TNNLS_2019_2927554 8784419
Genre	orig-research Journal Article
GrantInformation_xml	– fundername: National Natural Science Foundation of China grantid: 61673141; 61873148; 61773144 funderid: 10.13039/501100001809 – fundername: Alexander von Humboldt Foundation of Germany funderid: 10.13039/100005156 – fundername: Fok Ying Tung Education Foundation of China grantid: 151004 funderid: 10.13039/501100010261 – fundername: Outstanding Youth Science Foundation of Heilongjiang Province of China grantid: JC2018001
GroupedDBID	0R~ 4.4 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK ACPRK AENEX AFRAH AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD IFIPE IPLJI JAVBF M43 MS~ O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION NPM RIG 7QF 7QO 7QP 7QQ 7QR 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8
ID	FETCH-LOGICAL-c395t-d3b6165e7eae9768d357fdc215da2a71b25672d24a2d703decbe36fa1e65244c3
IEDL.DBID	RIE
ISICitedReferencesCount	139
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000542953000014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2162-237X 2162-2388
IngestDate	Thu Oct 02 12:03:51 EDT 2025 Mon Jun 30 06:31:32 EDT 2025 Thu Apr 03 06:52:02 EDT 2025 Sat Nov 29 01:40:03 EST 2025 Tue Nov 18 20:59:39 EST 2025 Wed Aug 27 02:39:01 EDT 2025
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	false
IsScholarly	true
Issue	6
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c395t-d3b6165e7eae9768d357fdc215da2a71b25672d24a2d703decbe36fa1e65244c3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-7852-5064 0000-0002-9576-7401 0000-0002-0699-2296
OpenAccessLink	http://bura.brunel.ac.uk/bitstream/2438/21884/1/FullText.pdf
PMID	31395561
PQID	2409379563
PQPubID	85436
PageCount	13
ParticipantIDs	crossref_citationtrail_10_1109_TNNLS_2019_2927554 crossref_primary_10_1109_TNNLS_2019_2927554 ieee_primary_8784419 proquest_miscellaneous_2270016067 proquest_journals_2409379563 pubmed_primary_31395561
PublicationCentury	2000
PublicationDate	2020-06-01
PublicationDateYYYYMMDD	2020-06-01
PublicationDate_xml	– month: 06 year: 2020 text: 2020-06-01 day: 01
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: Piscataway
PublicationTitle	IEEE transaction on neural networks and learning systems
PublicationTitleAbbrev	TNNLS
PublicationTitleAlternate	IEEE Trans Neural Netw Learn Syst
PublicationYear	2020
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref35 ref13 ref34 ref37 ref15 ref14 jin (ref18) 2012; 23 ref31 hardy (ref12) 1988 ref30 ref33 ref11 ref32 ref10 ref2 ref1 ref39 ref17 ref38 ref16 ref19 ref24 ref23 ref26 ref25 ref20 ref42 ref41 ref22 ref21 ref43 dattorro (ref8) 2005 ref28 ref27 ref29 ref7 ref9 ref4 ref3 ref6 ref5 ref40 wu (ref36) 2013; 24
References_xml	– ident: ref31 doi: 10.1109/TIE.2015.2475515 – ident: ref34 doi: 10.1038/srep26733 – ident: ref38 doi: 10.1109/TNNLS.2015.2503772 – ident: ref11 doi: 10.1049/iet-cta.2016.1161 – ident: ref29 doi: 10.1109/TNNLS.2013.2271357 – ident: ref9 doi: 10.1016/j.physa.2012.07.007 – year: 1988 ident: ref12 publication-title: Inequalities – ident: ref33 doi: 10.1109/TNNLS.2014.2387443 – ident: ref28 doi: 10.1109/9.754809 – ident: ref19 doi: 10.4173/mic.2009.1.3 – ident: ref27 doi: 10.1016/j.amc.2017.02.041 – ident: ref13 doi: 10.1016/j.inffus.2017.03.003 – ident: ref39 doi: 10.1109/TCYB.2016.2568281 – ident: ref5 doi: 10.1016/j.amc.2013.10.068 – ident: ref42 doi: 10.1016/j.neucom.2018.07.086 – ident: ref24 doi: 10.1137/060657935 – ident: ref22 doi: 10.1109/TCYB.2015.2399334 – ident: ref14 doi: 10.1016/j.automatica.2018.07.027 – ident: ref35 doi: 10.1016/0167-6911(92)90097-C – ident: ref32 doi: 10.1016/j.amc.2017.05.007 – volume: 24 start-page: 1177 year: 2013 ident: ref36 article-title: Sampled-data exponential synchronization of complex dynamical networks with time-varying coupling delay publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2013.2253122 – ident: ref3 doi: 10.1109/TIE.2017.2701776 – ident: ref16 doi: 10.1109/TFUZZ.2014.2350534 – ident: ref4 doi: 10.1016/j.physrep.2005.10.009 – ident: ref23 doi: 10.1016/j.jfranklin.2016.08.021 – ident: ref7 doi: 10.1109/TSP.2017.2686375 – volume: 23 start-page: 1345 year: 2012 ident: ref18 article-title: Adaptive pinning control of deteriorated nonlinear coupling networks with circuit realization publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2012.2202246 – ident: ref25 doi: 10.1109/TCYB.2015.2509170 – ident: ref40 doi: 10.1109/TCYB.2016.2536748 – ident: ref17 doi: 10.1016/j.neucom.2018.08.060 – ident: ref1 doi: 10.1080/00207721.2012.670309 – ident: ref20 doi: 10.1080/03081079.2014.973725 – year: 2005 ident: ref8 publication-title: Convex Optimization & Euclidean Distance Geometry – ident: ref30 doi: 10.1109/TNN.2010.2090669 – ident: ref43 doi: 10.1109/TCYB.2014.2386781 – ident: ref10 doi: 10.1109/TAC.2005.858689 – ident: ref26 doi: 10.1109/TCYB.2017.2729164 – ident: ref21 doi: 10.1109/TCYB.2017.2653242 – ident: ref41 doi: 10.1109/TNNLS.2017.2678681 – ident: ref37 doi: 10.1109/TIE.2016.2587246 – ident: ref6 doi: 10.1016/j.inffus.2016.06.008 – ident: ref2 doi: 10.1109/TMECH.2017.2700459 – ident: ref15 doi: 10.1016/j.automatica.2015.11.008
SSID	ssj0000605649
Score	2.6385546
Snippet	In this paper, a new recursive state estimation problem is discussed for a class of discrete time-varying stochastic complex networks with uncertain inner...
SourceID	proquest pubmed crossref ieee
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	1955
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
URI	https://ieeexplore.ieee.org/document/8784419 https://www.ncbi.nlm.nih.gov/pubmed/31395561 https://www.proquest.com/docview/2409379563 https://www.proquest.com/docview/2270016067
Volume	31
WOSCitedRecordID	wos000542953000014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Xplore customDbUrl: eissn: 2162-2388 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000605649 issn: 2162-237X databaseCode: RIE dateStart: 20120101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB61FQcutFCgaUtlJG7gNrET2zki1AqkEvFoYW9REk9gpSpbdTcV4ifwq5lxskFIgMQtUuxx5JmJvxnPA-BZltYe68pJE1dGppg66eoEJSaqNeh9Y9pQxPXcFoWbzfJ3G_BiyoVBxBB8hsf8GO7y_aLp2VV24qyj0zvfhE1rzZCrNflTYsLlJqBdlRgllbazdY5MnJ9cFMX5Rw7kyo9VrmyWpb-dQ6Gxyt8xZjhrzrb_7yt34N6IKcXLQQjuwwZ2D2B73a9BjOq7Cz8-kWHMXJbcpjM0h0AvPrDHnYPYRQCe4pSUfshnFARoBeeISJrI6VCCaV7hN1EMweNL8Xm--ire98Se-Xei9faXx3Epqs6LS1otxByIN9zjiwj0nAL85SFcnp1evHotx14MstF5tpJe1yYxGVqskBCM8zqzrW8IMPhKVTapCTpZ5VVaKU8_EY9Njdq0VYImIwTR6Eew1S063AMRa7JZ26RVrdW0UcalreOqe1wLkKyvNIJkzZmyGQuV85ZclcFgifMycLNkbpYjNyN4Ps25Hsp0_HP0LrNtGjlyLILDtQCUoyYvS0I8hODIitQRPJ1ekw7yxUrV4aKnMeH2nkxBG8HjQXAm2pogNrcg3f_zmgdwV7EFH_w6h7C1uunxCdxpblfz5c0RCfrMHQVB_wkSFPrx
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9QwDLfGQIIXBoxB2YAg8QbZ2qRN2sdp2rSJW8XHDe6tahsXTpp6aHdFiD-Bv3p22itCAiTeKjVxqthufnb8AfAyiSuHVZlKE5ZGxhinMq0ilBipxqBztWl8EdeJzfN0NsvebsDrMRcGEX3wGe7zo7_Ld4u6Y1fZQWpTOr2zG3AziWMV9tlao0clJGRuPN5VkVFSaTtbZ8mE2cE0zycfOJQr21eZskkS_3YS-dYqf0eZ_rQ52fq_77wHdwdUKQ57MbgPG9g-gK11xwYxKPA2_PxIpjHzWXKjTt8eAp14zz53DmMXHnqKY1L7PqNREKQVnCUiaSInRAmmeYnfRd6Hjy_Fp_nqi3jXEYPmP4jW-S-f41KUrRMXtJqPOhBn3OWLCHScBPz5IVycHE-PTuXQjUHWOktW0unKRCZBiyUShkmdTmzjaoIMrlSljSoCT1Y5FZfK0W_EYV2hNk0ZoUkIQ9R6BzbbRYuPQYSarNYmalRjNW2USeMm5bp7XA2Q7K84gGjNmaIeSpXzllwW3mQJs8Jzs2BuFgM3A3g1zvnaF-r45-htZts4cuBYAHtrASgGXV4WhHkIw5EdqQN4Mb4mLeSrlbLFRUdj_P09GYM2gEe94Iy0NYFsbkL65M9rPofbp9PzSTE5y9_swh3F9rz38uzB5uqqw6dwq_62mi-vnnlxvwZVYf1Q
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Variance-Constrained+Recursive+State+Estimation+for+Time-Varying+Complex+Networks+With+Quantized+Measurements+and+Uncertain+Inner+Coupling&rft.jtitle=IEEE+transaction+on+neural+networks+and+learning+systems&rft.au=Hu%2C+Jun&rft.au=Wang%2C+Zidong&rft.au=Guo-Ping%2C+Liu&rft.au=Zhang%2C+Hongxu&rft.date=2020-06-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=2162-237X&rft.eissn=2162-2388&rft.volume=31&rft.issue=6&rft.spage=1955&rft_id=info:doi/10.1109%2FTNNLS.2019.2927554&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2162-237X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2162-237X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2162-237X&client=summon