Synchronization of Complex Dynamical Networks Subject to Noisy Sampling Interval and Packet Loss
This article focuses on the sampled-data synchronization issue for a class of complex dynamical networks (CDNs) subject to noisy sampling intervals and successive packet losses. The sampling intervals are subject to noisy perturbations, and categorical distribution is used to characterize the sampli...
Saved in:
| Published in | IEEE transaction on neural networks and learning systems Vol. 33; no. 8; pp. 3216 - 3226 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | This article focuses on the sampled-data synchronization issue for a class of complex dynamical networks (CDNs) subject to noisy sampling intervals and successive packet losses. The sampling intervals are subject to noisy perturbations, and categorical distribution is used to characterize the sampling errors of noisy sampling intervals. By means of the input delay approach, the CDN under consideration is first converted into a delay system with delayed input subject to dual randomness and probability distribution characteristic. To verify the probability distribution characteristic of the delayed input, a novel characterization method is proposed, which is not the same as that of some existing literature. Based on this, a unified framework is then established. By recurring to the techniques of stochastic analysis, a probability-distribution-dependent controller is designed to guarantee the mean-square exponential synchronization of the error dynamical network. Subsequently, a special model is considered where only the lower and upper bounds of delayed input are utilized. Finally, to verify the analysis results and testify the effectiveness and superiority of the designed synchronization algorithm, a numerical example and an example using Chua's circuit are given. |
|---|---|
| AbstractList | This article focuses on the sampled-data synchronization issue for a class of complex dynamical networks (CDNs) subject to noisy sampling intervals and successive packet losses. The sampling intervals are subject to noisy perturbations, and categorical distribution is used to characterize the sampling errors of noisy sampling intervals. By means of the input delay approach, the CDN under consideration is first converted into a delay system with delayed input subject to dual randomness and probability distribution characteristic. To verify the probability distribution characteristic of the delayed input, a novel characterization method is proposed, which is not the same as that of some existing literature. Based on this, a unified framework is then established. By recurring to the techniques of stochastic analysis, a probability-distribution-dependent controller is designed to guarantee the mean-square exponential synchronization of the error dynamical network. Subsequently, a special model is considered where only the lower and upper bounds of delayed input are utilized. Finally, to verify the analysis results and testify the effectiveness and superiority of the designed synchronization algorithm, a numerical example and an example using Chua's circuit are given. This article focuses on the sampled-data synchronization issue for a class of complex dynamical networks (CDNs) subject to noisy sampling intervals and successive packet losses. The sampling intervals are subject to noisy perturbations, and categorical distribution is used to characterize the sampling errors of noisy sampling intervals. By means of the input delay approach, the CDN under consideration is first converted into a delay system with delayed input subject to dual randomness and probability distribution characteristic. To verify the probability distribution characteristic of the delayed input, a novel characterization method is proposed, which is not the same as that of some existing literature. Based on this, a unified framework is then established. By recurring to the techniques of stochastic analysis, a probability-distribution-dependent controller is designed to guarantee the mean-square exponential synchronization of the error dynamical network. Subsequently, a special model is considered where only the lower and upper bounds of delayed input are utilized. Finally, to verify the analysis results and testify the effectiveness and superiority of the designed synchronization algorithm, a numerical example and an example using Chua's circuit are given.This article focuses on the sampled-data synchronization issue for a class of complex dynamical networks (CDNs) subject to noisy sampling intervals and successive packet losses. The sampling intervals are subject to noisy perturbations, and categorical distribution is used to characterize the sampling errors of noisy sampling intervals. By means of the input delay approach, the CDN under consideration is first converted into a delay system with delayed input subject to dual randomness and probability distribution characteristic. To verify the probability distribution characteristic of the delayed input, a novel characterization method is proposed, which is not the same as that of some existing literature. Based on this, a unified framework is then established. By recurring to the techniques of stochastic analysis, a probability-distribution-dependent controller is designed to guarantee the mean-square exponential synchronization of the error dynamical network. Subsequently, a special model is considered where only the lower and upper bounds of delayed input are utilized. Finally, to verify the analysis results and testify the effectiveness and superiority of the designed synchronization algorithm, a numerical example and an example using Chua's circuit are given. |
| Author | Shi, Peng Ren, Hongru Hu, Zhipei |
| Author_xml | – sequence: 1 givenname: Zhipei orcidid: 0000-0001-6494-8608 surname: Hu fullname: Hu, Zhipei email: huzhipei5374@126.com organization: Department of Electronic and Information Engineering, College of Engineering, Shantou University, Shantou, China – sequence: 2 givenname: Hongru orcidid: 0000-0002-2524-4533 surname: Ren fullname: Ren, Hongru email: renhongru2019@gdut.edu.cn organization: School of Automation and the Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou, China – sequence: 3 givenname: Peng orcidid: 0000-0001-8218-586X surname: Shi fullname: Shi, Peng email: peng.shi@adelaide.edu.au organization: School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33481722$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kU1vEzEQhi1UREvpHwAJWeLCJWH8tWsfUaBQKQpIKRI343W84HRjp7aXEn49bpP20ANz8RyeZzSe9zk6CjE4hF4SmBIC6t3lYjFfTilQMmUgCAj6BJ1Q0tAJZVIePfTt92N0lvMaajUgGq6eoWPGuCQtpSfox3IX7K8Ug_9rio8Bxx7P4mY7uD_4wy6YjbdmwAtXbmK6yng5dmtnCy4RL6LPO7w0lfXhJ74IxaXflTVhhb8ae-UKnsecX6CnvRmyOzu8p-jb-cfL2efJ_Muni9n7-cQyQcpEGdlQ3limFIhecil47zoiescbQqGlAjqAtu247RU4pQQQQVbCKqAAlrFT9HY_d5vi9ehy0RufrRsGE1wcs6ZcAhWKKVHRN4_QdRxTqNtp2qhWNIpLWanXB2rsNm6lt8lvTNrp-9tVQO4Bm-o_k-u19eXuiCUZP2gC-jYpfZeUvk1KH5KqKn2k3k__r_RqL3nn3IOg6j6cSPYPnBWcaw |
| CODEN | ITNNAL |
| CitedBy_id | crossref_primary_10_1080_00207721_2024_2437672 crossref_primary_10_1109_TNNLS_2022_3167139 crossref_primary_10_1109_TCSII_2021_3079354 crossref_primary_10_1109_TAC_2024_3422090 crossref_primary_10_1109_TNNLS_2023_3304515 crossref_primary_10_1002_rnc_7163 crossref_primary_10_1109_TCNS_2024_3488515 crossref_primary_10_3934_mbe_2023144 crossref_primary_10_1007_s00034_022_02059_7 crossref_primary_10_1109_ACCESS_2023_3270280 crossref_primary_10_1002_rnc_6512 crossref_primary_10_1109_JSYST_2023_3333823 crossref_primary_10_1109_TSMC_2023_3341441 crossref_primary_10_1049_cth2_12676 crossref_primary_10_3390_math12060917 crossref_primary_10_1109_TSMC_2022_3212018 crossref_primary_10_1109_TSMC_2024_3509629 crossref_primary_10_1002_acs_3713 crossref_primary_10_1109_TSMC_2024_3502677 crossref_primary_10_1109_TAC_2023_3282849 crossref_primary_10_1016_j_neunet_2023_02_031 crossref_primary_10_14232_ejqtde_2022_1_49 crossref_primary_10_1109_TCNS_2022_3158817 crossref_primary_10_1115_1_4066121 crossref_primary_10_1016_j_automatica_2022_110806 crossref_primary_10_1016_j_jfranklin_2021_08_015 crossref_primary_10_1109_TCNS_2022_3141696 crossref_primary_10_1002_rnc_5490 crossref_primary_10_1080_00207721_2024_2328776 crossref_primary_10_1016_j_jfranklin_2024_106684 crossref_primary_10_1007_s40815_024_01880_3 crossref_primary_10_1109_TCYB_2022_3233065 |
| Cites_doi | 10.1109/TNNLS.2016.2515080 10.1109/TSMCA.2009.2019875 10.1109/TSP.2012.2190599 10.1016/j.amc.2015.03.033 10.1109/TNNLS.2016.2614709 10.1109/9.827351 10.1109/TSMCB.2011.2163797 10.1109/TCSI.2003.818611 10.1109/TNNLS.2013.2253122 10.1002/rnc.2779 10.1109/TAC.2017.2685083 10.1109/TSMC.2016.2563393 10.1109/TAC.2017.2676986 10.1016/j.jfranklin.2019.12.010 10.1109/TNNLS.2015.2412676 10.1002/rnc.3302 10.1109/TPWRS.2015.2485272 10.1016/j.automatica.2015.10.005 10.1002/rnc.3559 10.1016/j.automatica.2009.03.004 10.1016/j.amc.2018.11.017 10.1109/TIE.2019.2928241 10.1016/j.automatica.2018.04.047 10.1016/j.neunet.2013.05.001 10.1007/s10957-011-9917-0 10.1109/TNNLS.2019.2928039 10.1016/j.jfranklin.2017.04.016 10.1016/j.amc.2018.10.088 10.1016/j.automatica.2008.02.028 10.1109/TAC.2012.2190179 10.1038/30918 10.1002/rnc.3087 10.1109/TSMC.2017.2781234 10.1109/TIFS.2014.2299404 10.1109/TSMCB.2008.2007496 10.1016/j.physa.2015.03.065 10.1080/00207721.2014.919426 10.1109/TNNLS.2018.2869375 10.1038/35065725 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| 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.2021.3051052 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005-present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) 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 | PubMed Materials Research Database MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 2162-2388 |
| EndPage | 3226 |
| ExternalDocumentID | 33481722 10_1109_TNNLS_2021_3051052 9334418 |
| Genre | orig-research Journal Article |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 62003204; 62003093 funderid: 10.13039/501100001809 – fundername: Shantou University Scientific Research Foundation for Talents grantid: NTF19031 funderid: 10.13039/100009047 – fundername: Australian Research Council grantid: DP170102644 funderid: 10.13039/501100000923 |
| 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 RIG 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 |
| ID | FETCH-LOGICAL-c351t-9a86246c39905f84854feb15fe461207250b0077b4cf90e9950151d5c90200c33 |
| IEDL.DBID | RIE |
| ISSN | 2162-237X 2162-2388 |
| IngestDate | Fri Jul 11 09:01:21 EDT 2025 Mon Jun 30 04:13:46 EDT 2025 Thu Jan 02 22:56:24 EST 2025 Tue Jul 01 00:27:37 EDT 2025 Thu Apr 24 23:09:08 EDT 2025 Wed Aug 27 02:23:36 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 8 |
| 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-c351t-9a86246c39905f84854feb15fe461207250b0077b4cf90e9950151d5c90200c33 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-6494-8608 0000-0002-2524-4533 0000-0001-8218-586X |
| PMID | 33481722 |
| PQID | 2697569488 |
| PQPubID | 85436 |
| PageCount | 11 |
| ParticipantIDs | pubmed_primary_33481722 crossref_citationtrail_10_1109_TNNLS_2021_3051052 crossref_primary_10_1109_TNNLS_2021_3051052 ieee_primary_9334418 proquest_miscellaneous_2480259395 proquest_journals_2697569488 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2022-08-01 |
| PublicationDateYYYYMMDD | 2022-08-01 |
| PublicationDate_xml | – month: 08 year: 2022 text: 2022-08-01 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 | 2022 |
| 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 | ref34 ref37 ref15 ref36 ref14 ref31 ref33 ref11 ref32 ref10 su (ref13) 2015; 259 ref2 ref1 ref39 ref17 ref38 ref16 ref19 ref18 tirandaz (ref7) 2018; 9 hu (ref28) 2020 huang (ref9) 2020; 16 ref24 ref23 ref26 ref25 ref20 ref42 ref41 ref22 ref21 ref43 yue (ref29) 2009; 39 ref27 ref8 ref4 ref3 wu (ref12) 2013; 24 ref6 yue (ref30) 2009; 39 ref5 murphy (ref35) 2012 ref40 |
| References_xml | – ident: ref6 doi: 10.1109/TNNLS.2016.2515080 – volume: 39 start-page: 939 year: 2009 ident: ref29 article-title: Stabilization of systems with probabilistic interval input delays and its applications to networked control systems publication-title: IEEE Trans Syst Man Cybern A Syst Humans doi: 10.1109/TSMCA.2009.2019875 – ident: ref23 doi: 10.1109/TSP.2012.2190599 – volume: 259 start-page: 931 year: 2015 ident: ref13 article-title: Mixed ${H}_\infty$ /passive synchronization for complex dynamical networks with sampled-data control publication-title: Appl Math Comput doi: 10.1016/j.amc.2015.03.033 – ident: ref14 doi: 10.1109/TNNLS.2016.2614709 – ident: ref36 doi: 10.1109/9.827351 – ident: ref22 doi: 10.1109/TSMCB.2011.2163797 – ident: ref3 doi: 10.1109/TCSI.2003.818611 – volume: 24 start-page: 1177 year: 2013 ident: ref12 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 – year: 2020 ident: ref28 article-title: Synchronization of stochastic complex dynamical networks subject to consecutive packet dropouts publication-title: IEEE Trans Cybern – ident: ref39 doi: 10.1002/rnc.2779 – volume: 16 start-page: 123 year: 2020 ident: ref9 article-title: $L_{2}$ - $L_\infty$ filtering for networked switched systems with multiple packet dropouts via random switched Lyapunov function publication-title: Int J Innov Comput Inf Control – ident: ref21 doi: 10.1109/TAC.2017.2685083 – ident: ref24 doi: 10.1109/TSMC.2016.2563393 – ident: ref26 doi: 10.1109/TAC.2017.2676986 – ident: ref19 doi: 10.1016/j.jfranklin.2019.12.010 – ident: ref5 doi: 10.1109/TNNLS.2015.2412676 – ident: ref38 doi: 10.1002/rnc.3302 – ident: ref31 doi: 10.1109/TPWRS.2015.2485272 – ident: ref20 doi: 10.1016/j.automatica.2015.10.005 – year: 2012 ident: ref35 publication-title: Machine Learning A Probabilistic Perspective – ident: ref25 doi: 10.1002/rnc.3559 – ident: ref40 doi: 10.1016/j.automatica.2009.03.004 – ident: ref8 doi: 10.1016/j.amc.2018.11.017 – ident: ref11 doi: 10.1109/TIE.2019.2928241 – ident: ref43 doi: 10.1016/j.automatica.2018.04.047 – ident: ref41 doi: 10.1016/j.neunet.2013.05.001 – ident: ref33 doi: 10.1007/s10957-011-9917-0 – ident: ref17 doi: 10.1109/TNNLS.2019.2928039 – ident: ref27 doi: 10.1016/j.jfranklin.2017.04.016 – ident: ref16 doi: 10.1016/j.amc.2018.10.088 – ident: ref18 doi: 10.1016/j.automatica.2008.02.028 – ident: ref42 doi: 10.1109/TAC.2012.2190179 – volume: 9 start-page: 11 year: 2018 ident: ref7 article-title: Master-slave synchronization of zhang and lorenz chaotic systems with uncertain parameters, an active nonlinear feedback controller publication-title: ICIC Exp Lett B Appl Int J Res Surv – ident: ref1 doi: 10.1038/30918 – ident: ref32 doi: 10.1002/rnc.3087 – ident: ref15 doi: 10.1109/TSMC.2017.2781234 – ident: ref4 doi: 10.1109/TIFS.2014.2299404 – volume: 39 start-page: 503 year: 2009 ident: ref30 article-title: Delay-distribution-dependent stability and stabilization of T-S fuzzy systems with probabilistic interval delay publication-title: IEEE Trans Syst Man Cybern B Cybern doi: 10.1109/TSMCB.2008.2007496 – ident: ref37 doi: 10.1016/j.physa.2015.03.065 – ident: ref34 doi: 10.1080/00207721.2014.919426 – ident: ref10 doi: 10.1109/TNNLS.2018.2869375 – ident: ref2 doi: 10.1038/35065725 |
| SSID | ssj0000605649 |
| Score | 2.5465603 |
| Snippet | This article focuses on the sampled-data synchronization issue for a class of complex dynamical networks (CDNs) subject to noisy sampling intervals and... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 3216 |
| SubjectTerms | Algorithms Circuits Complex dynamical networks (CDNs) Control systems design Delays Intervals Noise measurement noisy sampling interval Packet loss Perturbation Perturbation methods Probability distribution Randomness Sampling Sampling error Stochasticity successive packet losses Synchronism Synchronization synchronization control Upper bound Upper bounds |
| Title | Synchronization of Complex Dynamical Networks Subject to Noisy Sampling Interval and Packet Loss |
| URI | https://ieeexplore.ieee.org/document/9334418 https://www.ncbi.nlm.nih.gov/pubmed/33481722 https://www.proquest.com/docview/2697569488 https://www.proquest.com/docview/2480259395 |
| Volume | 33 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3NT90wDLeAE5cxxjY62JRJ3EYfbZq0zRENEJqgmvRAerfOSVOEQO201yfB_vo56Yc0tCFulZp-2nF-dvyzAQ5EhJKjxRArMoFCkJ-CtZChNYT1hdZKWMd3vizS82vxbSEXa3A4cWGstT75zM7cod_Lr1qzcqGyI3K-afXO12GdHLeeqzXFUyLC5alHuzxOeciTbDFyZCJ1dFUUF3PyBnk8S5waStfFxpNQM87_WpJ8j5X_w02_7JxtweX4wn22yd1s1emZ-f2kluNLv-g1vBrwJzvuFWYb1mzzBrbG3g5smOo78GP-2BhfOLfnabK2Zm7UvX1gJ30Xe7pN0SeRLxnZHxfQYV3LivZ2-cjm6FLVmxvmQ46kzgybin1HMhodu6Df8Bauz06vvp6HQzeG0CQy7kKFjkuSGkI0kaxzkUtRk6GXtRWEkqKMsJR2xYG0MLWKrFKSkEZcSaMIkUYmSd7BRtM2dheYEhnmNRcaEUWsM03AQdfSIOYVR64CiEeBlGYoVe46ZtyX3mWJVOnlWTp5loM8A_gyXfOzL9Tx7OgdJ4xp5CCHAPZHuZfDXF6WPFWZTBVZugA-T6dpFrqtFWxsu6IxIifwqBIlA3jf68t071HNPvz7mXuwyR2lwicV7sNG92tlPxLQ6fQnr-F_APaM9eI |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB6VcoALLZRHoICRuEG2iWMn8REVqgV2I6TdSnsLY8dBVasEsVmJ8usZOw8JBIhbpEyeM575ZjwPgJciQsnRYogVqUAhyE_BWsjQGsL6QmslrKt3Xhbp_Fx82MjNHryeamGstT75zM7cod_Lr1qzc6GyE3K-yXrnN-Am2X0Z99VaU0QlImSeerzL45SHPMk2Y5VMpE7WRbFYkT_I41niBFG6OTa-DDXj_Bej5Kes_B1wesNzdgDL8ZX7fJPL2a7TM_Pjt26O__tNh3BnQKDsTS8yd2HPNvfgYJzuwIbFfgSfV9eN8a1z-0pN1tbMUV3Z7-xtP8eeblP0aeRbRhrIhXRY17KivdhesxW6ZPXmC_NBRxJohk3FPiGpjY4t6Dfch_Ozd-vTeTjMYwhNIuMuVOiqSVJDmCaSdS5yKWpS9bK2gnBSlBGa0q49kBamVpFVShLWiCtpFGHSyCTJA9hv2sY-AqZEhnnNhUZEEetME3TQtTSIecWRqwDikSGlGZqVu5kZV6V3WiJVen6Wjp_lwM8AXk3XfO1bdfyT-sgxY6Ic-BDA8cj3cljN25KnKpOpIl0XwIvpNK1Dt7mCjW13RCNygo8qUTKAh728TPcexezxn5_5HG7N18tFuXhffHwCt7krsPAphsew333b2acEezr9zEv7T_HJ-Ss |
| 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=Synchronization+of+Complex+Dynamical+Networks+Subject+to+Noisy+Sampling+Interval+and+Packet+Loss&rft.jtitle=IEEE+transaction+on+neural+networks+and+learning+systems&rft.au=Hu%2C+Zhipei&rft.au=Ren%2C+Hongru&rft.au=Shi%2C+Peng&rft.date=2022-08-01&rft.issn=2162-237X&rft.eissn=2162-2388&rft.volume=33&rft.issue=8&rft.spage=3216&rft.epage=3226&rft_id=info:doi/10.1109%2FTNNLS.2021.3051052&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TNNLS_2021_3051052 |
| 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 |