Passivity analysis for memristor-based recurrent neural networks with discrete and distributed delays
In this paper, based on the knowledge of memristor and recurrent neural networks (RNNs), the model of the memristor-based RNNs with discrete and distributed delays is established. By constructing proper Lyapunov functionals and using inequality technique, several sufficient conditions are given to e...
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| Veröffentlicht in: | Neural networks 2015-01, Vol.61, p.49-58 |
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| creator | Zhang, Guodong Shen, Yi Yin, Quan Sun, Junwei |
| description | In this paper, based on the knowledge of memristor and recurrent neural networks (RNNs), the model of the memristor-based RNNs with discrete and distributed delays is established. By constructing proper Lyapunov functionals and using inequality technique, several sufficient conditions are given to ensure the passivity of the memristor-based RNNs with discrete and distributed delays in the sense of Filippov solutions. The passivity conditions here are presented in terms of linear matrix inequalities, which can be easily solved by using Matlab Tools. In addition, the results of this paper complement and extend the earlier publications. Finally, numerical simulations are employed to illustrate the effectiveness of the obtained results. |
| doi_str_mv | 10.1016/j.neunet.2014.10.004 |
| format | Article |
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Finally, numerical simulations are employed to illustrate the effectiveness of the obtained results.</description><identifier>ISSN: 0893-6080</identifier><identifier>EISSN: 1879-2782</identifier><identifier>DOI: 10.1016/j.neunet.2014.10.004</identifier><identifier>PMID: 25462633</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Algorithms ; Filippov solution ; Memristor ; Models, Theoretical ; Neural networks ; Neural Networks (Computer) ; Passivity ; Time delay</subject><ispartof>Neural networks, 2015-01, Vol.61, p.49-58</ispartof><rights>2014 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c432t-2693182b0c1e48f981448780aa8bcc1221f80350722249f8cf26effbcdccf9bf3</citedby><cites>FETCH-LOGICAL-c432t-2693182b0c1e48f981448780aa8bcc1221f80350722249f8cf26effbcdccf9bf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.neunet.2014.10.004$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3549,27923,27924,45994</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25462633$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Guodong</creatorcontrib><creatorcontrib>Shen, Yi</creatorcontrib><creatorcontrib>Yin, Quan</creatorcontrib><creatorcontrib>Sun, Junwei</creatorcontrib><title>Passivity analysis for memristor-based recurrent neural networks with discrete and distributed delays</title><title>Neural networks</title><addtitle>Neural Netw</addtitle><description>In this paper, based on the knowledge of memristor and recurrent neural networks (RNNs), the model of the memristor-based RNNs with discrete and distributed delays is established. 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Finally, numerical simulations are employed to illustrate the effectiveness of the obtained results.</description><subject>Algorithms</subject><subject>Filippov solution</subject><subject>Memristor</subject><subject>Models, Theoretical</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Passivity</subject><subject>Time delay</subject><issn>0893-6080</issn><issn>1879-2782</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kMtLAzEQxoMoWh__gcgevWzNq2n2Ioj4AkEPeg7Z7ART91Ez2Zb-96ZUPXoaZvi--WZ-hJwzOmWUqavFtIexhzTllMk8mlIq98iE6XlV8rnm-2RCdSVKRTU9IseIC0qp0lIckiM-k4orISYEXi1iWIW0KWxv2w0GLPwQiw66GDANsawtQlNEcGOM0Kcip0bb5pLWQ_zEYh3SR9EEdBES5CXNtkkx1GPKvgZau8FTcuBti3D2U0_I-_3d2-1j-fzy8HR781w6KXgquaoE07ymjoHUvtJMSj3X1FpdO8c4Z15TMaNzzrmsvHaeK_C-do1zvqq9OCGXu73LOHyNgMl0-TBoW9vDMKJhSnHJMxWRpXIndXFAjODNMobOxo1h1GwBm4XZATZbwNtpBpxtFz8JY91B82f6JZoF1zsB5D9XAaJBF6B30ITMMJlmCP8nfAOCE5Ch</recordid><startdate>201501</startdate><enddate>201501</enddate><creator>Zhang, Guodong</creator><creator>Shen, Yi</creator><creator>Yin, Quan</creator><creator>Sun, Junwei</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>201501</creationdate><title>Passivity analysis for memristor-based recurrent neural networks with discrete and distributed delays</title><author>Zhang, Guodong ; Shen, Yi ; Yin, Quan ; Sun, Junwei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c432t-2693182b0c1e48f981448780aa8bcc1221f80350722249f8cf26effbcdccf9bf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Filippov solution</topic><topic>Memristor</topic><topic>Models, Theoretical</topic><topic>Neural networks</topic><topic>Neural Networks (Computer)</topic><topic>Passivity</topic><topic>Time delay</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Guodong</creatorcontrib><creatorcontrib>Shen, Yi</creatorcontrib><creatorcontrib>Yin, Quan</creatorcontrib><creatorcontrib>Sun, Junwei</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Neural networks</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Guodong</au><au>Shen, Yi</au><au>Yin, Quan</au><au>Sun, Junwei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Passivity analysis for memristor-based recurrent neural networks with discrete and distributed delays</atitle><jtitle>Neural networks</jtitle><addtitle>Neural Netw</addtitle><date>2015-01</date><risdate>2015</risdate><volume>61</volume><spage>49</spage><epage>58</epage><pages>49-58</pages><issn>0893-6080</issn><eissn>1879-2782</eissn><abstract>In this paper, based on the knowledge of memristor and recurrent neural networks (RNNs), the model of the memristor-based RNNs with discrete and distributed delays is established. 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| language | eng |
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| source | MEDLINE; ScienceDirect Journals (5 years ago - present) |
| subjects | Algorithms Filippov solution Memristor Models, Theoretical Neural networks Neural Networks (Computer) Passivity Time delay |
| title | Passivity analysis for memristor-based recurrent neural networks with discrete and distributed delays |
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