Pinning Synchronization of Directed Coupled Reaction-Diffusion Neural Networks With Sampled-Data Communications
This paper focuses on the design of a pinning sampled-data control mechanism for the exponential synchronization of directed coupled reaction-diffusion neural networks (CRDNNs) with sampled-data communications (SDCs). A new Lyapunov-Krasovskii functional (LKF) with some sampled-instant-dependent ter...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2020-06, Vol.31 (6), p.2092-2103 |
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| creator | Zeng, Deqiang Zhang, Ruimei Park, Ju H. Pu, Zhilin Liu, Yajuan |
| description | This paper focuses on the design of a pinning sampled-data control mechanism for the exponential synchronization of directed coupled reaction-diffusion neural networks (CRDNNs) with sampled-data communications (SDCs). A new Lyapunov-Krasovskii functional (LKF) with some sampled-instant-dependent terms is presented, which can fully utilize the actual sampling information. Then, an inequality is first proposed, which effectively relaxes the restrictions of the positive definiteness of the constructed LKF. Based on the LKF and the inequality, sufficient conditions are derived to exponentially synchronize the directed CRDNNs with SDCs. The desired pinning sampled-data control gain is precisely obtained by solving some linear matrix inequalities (LMIs). Moreover, a less conservative exponential synchronization criterion is also established for directed coupled neural networks with SDCs. Finally, simulation results are provided to verify the effectiveness and merits of the theoretical results. |
| doi_str_mv | 10.1109/TNNLS.2019.2928039 |
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A new Lyapunov-Krasovskii functional (LKF) with some sampled-instant-dependent terms is presented, which can fully utilize the actual sampling information. Then, an inequality is first proposed, which effectively relaxes the restrictions of the positive definiteness of the constructed LKF. Based on the LKF and the inequality, sufficient conditions are derived to exponentially synchronize the directed CRDNNs with SDCs. The desired pinning sampled-data control gain is precisely obtained by solving some linear matrix inequalities (LMIs). Moreover, a less conservative exponential synchronization criterion is also established for directed coupled neural networks with SDCs. Finally, simulation results are provided to verify the effectiveness and merits of the theoretical results.</description><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNNLS.2019.2928039</identifier><identifier>PMID: 31395566</identifier><identifier>CODEN: ITNNAL</identifier><language>eng</language><publisher>PISCATAWAY: IEEE</publisher><subject>Artificial neural networks ; Computer Science ; Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Computer simulation ; Data communication ; Delays ; Diffusion ; Directed coupled reaction-diffusion neural networks (CRDNNs) ; Engineering ; Engineering, Electrical & Electronic ; exponential synchronization ; Learning systems ; Linear matrix inequalities ; linear matrix inequalities (LMIs) ; Mathematical analysis ; Neural networks ; Pinning ; pinning sampled-data control ; sampled-data communications (SDCs) ; Science & Technology ; Synchronism ; Synchronization ; Technology</subject><ispartof>IEEE transaction on neural networks and learning systems, 2020-06, Vol.31 (6), p.2092-2103</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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A new Lyapunov-Krasovskii functional (LKF) with some sampled-instant-dependent terms is presented, which can fully utilize the actual sampling information. Then, an inequality is first proposed, which effectively relaxes the restrictions of the positive definiteness of the constructed LKF. Based on the LKF and the inequality, sufficient conditions are derived to exponentially synchronize the directed CRDNNs with SDCs. The desired pinning sampled-data control gain is precisely obtained by solving some linear matrix inequalities (LMIs). Moreover, a less conservative exponential synchronization criterion is also established for directed coupled neural networks with SDCs. Finally, simulation results are provided to verify the effectiveness and merits of the theoretical results.</description><subject>Artificial neural networks</subject><subject>Computer Science</subject><subject>Computer Science, Artificial Intelligence</subject><subject>Computer Science, Hardware & Architecture</subject><subject>Computer Science, Theory & Methods</subject><subject>Computer simulation</subject><subject>Data communication</subject><subject>Delays</subject><subject>Diffusion</subject><subject>Directed coupled reaction-diffusion neural networks (CRDNNs)</subject><subject>Engineering</subject><subject>Engineering, Electrical & Electronic</subject><subject>exponential synchronization</subject><subject>Learning systems</subject><subject>Linear matrix inequalities</subject><subject>linear matrix inequalities (LMIs)</subject><subject>Mathematical analysis</subject><subject>Neural networks</subject><subject>Pinning</subject><subject>pinning sampled-data control</subject><subject>sampled-data communications (SDCs)</subject><subject>Science & Technology</subject><subject>Synchronism</subject><subject>Synchronization</subject><subject>Technology</subject><issn>2162-237X</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>AOWDO</sourceid><recordid>eNqNkV2L1DAUhoso7rLuH1CQgjeCdMx30kuZ8QuGUZwVvStpeupmbZPZJGVZf73pzDiCV-bmBPK8h5PzFMVTjBYYo_r11Waz3i4IwvWC1EQhWj8ozgkWpCJUqYenu_x-VlzGeIPyEYgLVj8uziimNedCnBf-s3XOuh_l9t6Z6-Cd_aWT9a70fbmyAUyCrlz6aTfk-gW0mR-rle37Kc7YBqagh1zSnQ8_Y_nNputyq8eZr1Y66Rwex8lZs28bnxSPej1EuDzWi-Lru7dXyw_V-tP7j8s368pQjlMlORDBcc8EQh1lkrfItBgM6yTrCAOlRAcYG5AC91y2rTA6fw46BYQg2dGL4uWh7y742wliakYbDQyDduCn2BAiEcJMCpLRF_-gN34KLk_XEIZqqhCSPFPkQJngYwzQN7tgRx3uG4ya2UizN9LMRpqjkRx6fmw9tSN0p8if_Wfg1QG4g9b30VhwBk5YVsYZqTmd5ZF5BvX_9NKm_cqzPJdy9NkhagH-RpRUirGa_gaG1rDW</recordid><startdate>20200601</startdate><enddate>20200601</enddate><creator>Zeng, Deqiang</creator><creator>Zhang, Ruimei</creator><creator>Park, Ju H.</creator><creator>Pu, Zhilin</creator><creator>Liu, Yajuan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Zhang, Ruimei ; Park, Ju H. ; Pu, Zhilin ; Liu, Yajuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c351t-75e2651f4600d3475b0cb1ec4d74d24e886de11ce761f57bb6ca056ed8e2207d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial neural networks</topic><topic>Computer Science</topic><topic>Computer Science, Artificial Intelligence</topic><topic>Computer Science, Hardware & Architecture</topic><topic>Computer Science, Theory & Methods</topic><topic>Computer simulation</topic><topic>Data communication</topic><topic>Delays</topic><topic>Diffusion</topic><topic>Directed coupled reaction-diffusion neural networks (CRDNNs)</topic><topic>Engineering</topic><topic>Engineering, Electrical & Electronic</topic><topic>exponential synchronization</topic><topic>Learning systems</topic><topic>Linear matrix inequalities</topic><topic>linear matrix inequalities (LMIs)</topic><topic>Mathematical analysis</topic><topic>Neural networks</topic><topic>Pinning</topic><topic>pinning sampled-data control</topic><topic>sampled-data communications (SDCs)</topic><topic>Science & Technology</topic><topic>Synchronism</topic><topic>Synchronization</topic><topic>Technology</topic><toplevel>online_resources</toplevel><creatorcontrib>Zeng, Deqiang</creatorcontrib><creatorcontrib>Zhang, Ruimei</creatorcontrib><creatorcontrib>Park, Ju H.</creatorcontrib><creatorcontrib>Pu, Zhilin</creatorcontrib><creatorcontrib>Liu, Yajuan</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zeng, Deqiang</au><au>Zhang, Ruimei</au><au>Park, Ju H.</au><au>Pu, Zhilin</au><au>Liu, Yajuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pinning Synchronization of Directed Coupled Reaction-Diffusion Neural Networks With Sampled-Data Communications</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNNLS</stitle><stitle>IEEE T NEUR NET LEAR</stitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><date>2020-06-01</date><risdate>2020</risdate><volume>31</volume><issue>6</issue><spage>2092</spage><epage>2103</epage><pages>2092-2103</pages><issn>2162-237X</issn><eissn>2162-2388</eissn><coden>ITNNAL</coden><abstract>This paper focuses on the design of a pinning sampled-data control mechanism for the exponential synchronization of directed coupled reaction-diffusion neural networks (CRDNNs) with sampled-data communications (SDCs). A new Lyapunov-Krasovskii functional (LKF) with some sampled-instant-dependent terms is presented, which can fully utilize the actual sampling information. Then, an inequality is first proposed, which effectively relaxes the restrictions of the positive definiteness of the constructed LKF. Based on the LKF and the inequality, sufficient conditions are derived to exponentially synchronize the directed CRDNNs with SDCs. The desired pinning sampled-data control gain is precisely obtained by solving some linear matrix inequalities (LMIs). Moreover, a less conservative exponential synchronization criterion is also established for directed coupled neural networks with SDCs. Finally, simulation results are provided to verify the effectiveness and merits of the theoretical results.</abstract><cop>PISCATAWAY</cop><pub>IEEE</pub><pmid>31395566</pmid><doi>10.1109/TNNLS.2019.2928039</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-6996-5412</orcidid><orcidid>https://orcid.org/0000-0001-6885-9696</orcidid><orcidid>https://orcid.org/0000-0002-0218-2333</orcidid><orcidid>https://orcid.org/0000-0003-4986-8890</orcidid></addata></record> |
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| subjects | Artificial neural networks Computer Science Computer Science, Artificial Intelligence Computer Science, Hardware & Architecture Computer Science, Theory & Methods Computer simulation Data communication Delays Diffusion Directed coupled reaction-diffusion neural networks (CRDNNs) Engineering Engineering, Electrical & Electronic exponential synchronization Learning systems Linear matrix inequalities linear matrix inequalities (LMIs) Mathematical analysis Neural networks Pinning pinning sampled-data control sampled-data communications (SDCs) Science & Technology Synchronism Synchronization Technology |
| title | Pinning Synchronization of Directed Coupled Reaction-Diffusion Neural Networks With Sampled-Data Communications |
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