Passivity of Switched Recurrent Neural Networks With Time-Varying Delays
This paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time-varying delays. First, using the multiple Lyapunov functions method, a state-dependent switching law is designed to present a stochastic passivity condition. Second, a hystere...
Gespeichert in:
| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2015-02, Vol.26 (2), p.357-366 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 366 |
|---|---|
| container_issue | 2 |
| container_start_page | 357 |
| container_title | IEEE transaction on neural networks and learning systems |
| container_volume | 26 |
| creator | Lian, Jie Wang, Jun |
| description | This paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time-varying delays. First, using the multiple Lyapunov functions method, a state-dependent switching law is designed to present a stochastic passivity condition. Second, a hysteresis switching law involving both the current state and the previous value of the switching signal are presented to avoid chattering resulted from the state-dependent switching. Third, based on the average dwell-time approach, a class of switching signals is determined to guarantee the switched neural network stochastically passive. Finally, three numerical examples are provided to illustrate the characteristics of three proposed switching laws. |
| doi_str_mv | 10.1109/TNNLS.2014.2379920 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_1647300170</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7001700</ieee_id><sourcerecordid>1709183824</sourcerecordid><originalsourceid>FETCH-LOGICAL-c487t-b1ffde9a2a7d4801b3dd5658992e80cd51a229029043ea3b8e8a0ef2f4477c43</originalsourceid><addsrcrecordid>eNqNkUtPAjEQgBujEYL8AU3MJl68LPa12_Zo8IEJQSNEvW3K7qwUFxbbXQn_3vKQgyeaJtOk30w78yF0TnCHEKxuRoNBf9ihmPAOZUIpio9Qk5KYhpRJebw_i48Gajs3xX7FOIq5OkUNGkUijoRoot6Lds78mGoVlHkwXJoqnUAWvEJaWwvzKhhAbXXhQ7Us7ZcL3k01CUZmBuGbtisz_wzuoNArd4ZOcl04aO9iC40e7kfdXth_fnzq3vbDlEtRhWOS5xkoTbXIuMRkzLIsiiPpGwCJ0ywimlKF_eYMNBtLkBpDTnPOhUg5a6HrbdmFLb9rcFUyMy6FotBzKGuXEIEVkUzSA9A4VlKRmByCRpQpPzDl0at_6LSs7dy37CkuGMb-C56iWyq1pXMW8mRhzcwPLCE4WftLNv6Stb9k588nXe5K1-MZZPuUP1seuNgCBgD212LzJGa_P_mcjw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1647300170</pqid></control><display><type>article</type><title>Passivity of Switched Recurrent Neural Networks With Time-Varying Delays</title><source>IEEE Electronic Library (IEL)</source><creator>Lian, Jie ; Wang, Jun</creator><creatorcontrib>Lian, Jie ; Wang, Jun</creatorcontrib><description>This paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time-varying delays. First, using the multiple Lyapunov functions method, a state-dependent switching law is designed to present a stochastic passivity condition. Second, a hysteresis switching law involving both the current state and the previous value of the switching signal are presented to avoid chattering resulted from the state-dependent switching. Third, based on the average dwell-time approach, a class of switching signals is determined to guarantee the switched neural network stochastically passive. Finally, three numerical examples are provided to illustrate the characteristics of three proposed switching laws.</description><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNNLS.2014.2379920</identifier><identifier>PMID: 25576577</identifier><identifier>CODEN: ITNNAL</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Algorithms ; Artificial Intelligence ; Average dwell time ; Biological neural networks ; Computer Simulation ; Delay ; Delays ; Hysteresis ; hysteresis switching law ; Law ; Lyapunov functions ; Lyapunov methods ; Mathematical Computing ; Neural networks ; Neural Networks (Computer) ; Passivity ; Recurrent neural networks ; Stochasticity ; switched neural networks ; Switches ; Switching ; Symmetric matrices ; Time Factors ; Vibration</subject><ispartof>IEEE transaction on neural networks and learning systems, 2015-02, Vol.26 (2), p.357-366</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Feb 2015</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c487t-b1ffde9a2a7d4801b3dd5658992e80cd51a229029043ea3b8e8a0ef2f4477c43</citedby><cites>FETCH-LOGICAL-c487t-b1ffde9a2a7d4801b3dd5658992e80cd51a229029043ea3b8e8a0ef2f4477c43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7001700$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7001700$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25576577$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lian, Jie</creatorcontrib><creatorcontrib>Wang, Jun</creatorcontrib><title>Passivity of Switched Recurrent Neural Networks With Time-Varying Delays</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNNLS</addtitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><description>This paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time-varying delays. First, using the multiple Lyapunov functions method, a state-dependent switching law is designed to present a stochastic passivity condition. Second, a hysteresis switching law involving both the current state and the previous value of the switching signal are presented to avoid chattering resulted from the state-dependent switching. Third, based on the average dwell-time approach, a class of switching signals is determined to guarantee the switched neural network stochastically passive. Finally, three numerical examples are provided to illustrate the characteristics of three proposed switching laws.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Average dwell time</subject><subject>Biological neural networks</subject><subject>Computer Simulation</subject><subject>Delay</subject><subject>Delays</subject><subject>Hysteresis</subject><subject>hysteresis switching law</subject><subject>Law</subject><subject>Lyapunov functions</subject><subject>Lyapunov methods</subject><subject>Mathematical Computing</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Passivity</subject><subject>Recurrent neural networks</subject><subject>Stochasticity</subject><subject>switched neural networks</subject><subject>Switches</subject><subject>Switching</subject><subject>Symmetric matrices</subject><subject>Time Factors</subject><subject>Vibration</subject><issn>2162-237X</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqNkUtPAjEQgBujEYL8AU3MJl68LPa12_Zo8IEJQSNEvW3K7qwUFxbbXQn_3vKQgyeaJtOk30w78yF0TnCHEKxuRoNBf9ihmPAOZUIpio9Qk5KYhpRJebw_i48Gajs3xX7FOIq5OkUNGkUijoRoot6Lds78mGoVlHkwXJoqnUAWvEJaWwvzKhhAbXXhQ7Us7ZcL3k01CUZmBuGbtisz_wzuoNArd4ZOcl04aO9iC40e7kfdXth_fnzq3vbDlEtRhWOS5xkoTbXIuMRkzLIsiiPpGwCJ0ywimlKF_eYMNBtLkBpDTnPOhUg5a6HrbdmFLb9rcFUyMy6FotBzKGuXEIEVkUzSA9A4VlKRmByCRpQpPzDl0at_6LSs7dy37CkuGMb-C56iWyq1pXMW8mRhzcwPLCE4WftLNv6Stb9k588nXe5K1-MZZPuUP1seuNgCBgD212LzJGa_P_mcjw</recordid><startdate>20150201</startdate><enddate>20150201</enddate><creator>Lian, Jie</creator><creator>Wang, Jun</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><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>7QF</scope><scope>7QO</scope><scope>7QP</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TK</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20150201</creationdate><title>Passivity of Switched Recurrent Neural Networks With Time-Varying Delays</title><author>Lian, Jie ; Wang, Jun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c487t-b1ffde9a2a7d4801b3dd5658992e80cd51a229029043ea3b8e8a0ef2f4477c43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Average dwell time</topic><topic>Biological neural networks</topic><topic>Computer Simulation</topic><topic>Delay</topic><topic>Delays</topic><topic>Hysteresis</topic><topic>hysteresis switching law</topic><topic>Law</topic><topic>Lyapunov functions</topic><topic>Lyapunov methods</topic><topic>Mathematical Computing</topic><topic>Neural networks</topic><topic>Neural Networks (Computer)</topic><topic>Passivity</topic><topic>Recurrent neural networks</topic><topic>Stochasticity</topic><topic>switched neural networks</topic><topic>Switches</topic><topic>Switching</topic><topic>Symmetric matrices</topic><topic>Time Factors</topic><topic>Vibration</topic><toplevel>online_resources</toplevel><creatorcontrib>Lian, Jie</creatorcontrib><creatorcontrib>Wang, Jun</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>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</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>Lian, Jie</au><au>Wang, Jun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Passivity of Switched Recurrent Neural Networks With Time-Varying Delays</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNNLS</stitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><date>2015-02-01</date><risdate>2015</risdate><volume>26</volume><issue>2</issue><spage>357</spage><epage>366</epage><pages>357-366</pages><issn>2162-237X</issn><eissn>2162-2388</eissn><coden>ITNNAL</coden><abstract>This paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time-varying delays. First, using the multiple Lyapunov functions method, a state-dependent switching law is designed to present a stochastic passivity condition. Second, a hysteresis switching law involving both the current state and the previous value of the switching signal are presented to avoid chattering resulted from the state-dependent switching. Third, based on the average dwell-time approach, a class of switching signals is determined to guarantee the switched neural network stochastically passive. Finally, three numerical examples are provided to illustrate the characteristics of three proposed switching laws.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>25576577</pmid><doi>10.1109/TNNLS.2014.2379920</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2162-237X |
| ispartof | IEEE transaction on neural networks and learning systems, 2015-02, Vol.26 (2), p.357-366 |
| issn | 2162-237X 2162-2388 |
| language | eng |
| recordid | cdi_proquest_journals_1647300170 |
| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Artificial Intelligence Average dwell time Biological neural networks Computer Simulation Delay Delays Hysteresis hysteresis switching law Law Lyapunov functions Lyapunov methods Mathematical Computing Neural networks Neural Networks (Computer) Passivity Recurrent neural networks Stochasticity switched neural networks Switches Switching Symmetric matrices Time Factors Vibration |
| title | Passivity of Switched Recurrent Neural Networks With Time-Varying Delays |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-12T02%3A17%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Passivity%20of%20Switched%20Recurrent%20Neural%20Networks%20With%20Time-Varying%20Delays&rft.jtitle=IEEE%20transaction%20on%20neural%20networks%20and%20learning%20systems&rft.au=Lian,%20Jie&rft.date=2015-02-01&rft.volume=26&rft.issue=2&rft.spage=357&rft.epage=366&rft.pages=357-366&rft.issn=2162-237X&rft.eissn=2162-2388&rft.coden=ITNNAL&rft_id=info:doi/10.1109/TNNLS.2014.2379920&rft_dat=%3Cproquest_RIE%3E1709183824%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1647300170&rft_id=info:pmid/25576577&rft_ieee_id=7001700&rfr_iscdi=true |