Global asymptotically stability of cellular neural networks with time-varying delay
A set of criteria is presented for the global asymptotically stability of delayed cellular neural networks by constructing suitable Lyapunov functionals, introducing many parameters and combining with the elementary inequality technique. Time-delay is frequently encountered in neural networks, and i...
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| creator | Guozhuang Liang Xueli Wu Wenxia Du |
| description | A set of criteria is presented for the global asymptotically stability of delayed cellular neural networks by constructing suitable Lyapunov functionals, introducing many parameters and combining with the elementary inequality technique. Time-delay is frequently encountered in neural networks, and it is often a source of instability and oscillations in a system. It is very important to research the stability of delayed neural network, especially for global asymptotically stability of the neural network with time-varying delay. In the letter, a novel method is proposed in this note for global asymptotically stability of cellular neural networks with time-varying delay. New delay-dependent global asymptotically stability conditions of cellular neural network with time-varying delay is presented by constructing Lyapunov function and using linear matrix inequality (LMI). Finally, numerical examples are given to demonstrate the effect of the proposed method. |
| doi_str_mv | 10.1109/WCICA.2010.5554634 |
| format | Conference Proceeding |
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Time-delay is frequently encountered in neural networks, and it is often a source of instability and oscillations in a system. It is very important to research the stability of delayed neural network, especially for global asymptotically stability of the neural network with time-varying delay. In the letter, a novel method is proposed in this note for global asymptotically stability of cellular neural networks with time-varying delay. New delay-dependent global asymptotically stability conditions of cellular neural network with time-varying delay is presented by constructing Lyapunov function and using linear matrix inequality (LMI). Finally, numerical examples are given to demonstrate the effect of the proposed method.</description><identifier>EISBN: 9781424467129</identifier><identifier>EISBN: 142446711X</identifier><identifier>EISBN: 9781424467112</identifier><identifier>EISBN: 1424467128</identifier><identifier>DOI: 10.1109/WCICA.2010.5554634</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; Asymptotic stability ; Cellular neural networks ; Delay ; delayed cellular neural networks (DCNNs) ; global asymptotically stability ; LMI ; Lyapunov functional ; Numerical stability ; Stability criteria</subject><ispartof>2010 8th World Congress on Intelligent Control and Automation, 2010, p.5031-5036</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5554634$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5554634$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Guozhuang Liang</creatorcontrib><creatorcontrib>Xueli Wu</creatorcontrib><creatorcontrib>Wenxia Du</creatorcontrib><title>Global asymptotically stability of cellular neural networks with time-varying delay</title><title>2010 8th World Congress on Intelligent Control and Automation</title><addtitle>WCICA</addtitle><description>A set of criteria is presented for the global asymptotically stability of delayed cellular neural networks by constructing suitable Lyapunov functionals, introducing many parameters and combining with the elementary inequality technique. Time-delay is frequently encountered in neural networks, and it is often a source of instability and oscillations in a system. It is very important to research the stability of delayed neural network, especially for global asymptotically stability of the neural network with time-varying delay. In the letter, a novel method is proposed in this note for global asymptotically stability of cellular neural networks with time-varying delay. New delay-dependent global asymptotically stability conditions of cellular neural network with time-varying delay is presented by constructing Lyapunov function and using linear matrix inequality (LMI). Finally, numerical examples are given to demonstrate the effect of the proposed method.</description><subject>Artificial neural networks</subject><subject>Asymptotic stability</subject><subject>Cellular neural networks</subject><subject>Delay</subject><subject>delayed cellular neural networks (DCNNs)</subject><subject>global asymptotically stability</subject><subject>LMI</subject><subject>Lyapunov functional</subject><subject>Numerical stability</subject><subject>Stability criteria</subject><isbn>9781424467129</isbn><isbn>142446711X</isbn><isbn>9781424467112</isbn><isbn>1424467128</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj0FLxDAUhONBUNb-Ab3kD3Tte03T9ihFV2HBg4rH5TV90WjaLk3Wpf_ewu5pmGEYvhHiFrI1QFbffzYvzcMas8UXRaF0ri5EUpcVKFRKl4D1lUhC-MmyDEqtUeO1eNv4sSUvKcz9Po7RGfJ-liFS67yLsxytNOz9wdMkBz5MS3fgeByn3yCPLn7L6HpO_2ia3fAlO_Y034hLSz5wctaV-Hh6fG-e0-3rZiHcpg7KIqa1taABOy6x6iqj6641bAwhtoS5RQbAotO6RrtkljhvlxS6qgSolKF8Je5Ou46Zd_vJ9QvF7nw9_wdi3VFS</recordid><startdate>201007</startdate><enddate>201007</enddate><creator>Guozhuang Liang</creator><creator>Xueli Wu</creator><creator>Wenxia Du</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201007</creationdate><title>Global asymptotically stability of cellular neural networks with time-varying delay</title><author>Guozhuang Liang ; Xueli Wu ; Wenxia Du</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-9ff1612de728d8c69dbcecca22ba23f2e1125d6692fa22fae3b3f21d871184ca3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Artificial neural networks</topic><topic>Asymptotic stability</topic><topic>Cellular neural networks</topic><topic>Delay</topic><topic>delayed cellular neural networks (DCNNs)</topic><topic>global asymptotically stability</topic><topic>LMI</topic><topic>Lyapunov functional</topic><topic>Numerical stability</topic><topic>Stability criteria</topic><toplevel>online_resources</toplevel><creatorcontrib>Guozhuang Liang</creatorcontrib><creatorcontrib>Xueli Wu</creatorcontrib><creatorcontrib>Wenxia Du</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Guozhuang Liang</au><au>Xueli Wu</au><au>Wenxia Du</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Global asymptotically stability of cellular neural networks with time-varying delay</atitle><btitle>2010 8th World Congress on Intelligent Control and Automation</btitle><stitle>WCICA</stitle><date>2010-07</date><risdate>2010</risdate><spage>5031</spage><epage>5036</epage><pages>5031-5036</pages><eisbn>9781424467129</eisbn><eisbn>142446711X</eisbn><eisbn>9781424467112</eisbn><eisbn>1424467128</eisbn><abstract>A set of criteria is presented for the global asymptotically stability of delayed cellular neural networks by constructing suitable Lyapunov functionals, introducing many parameters and combining with the elementary inequality technique. Time-delay is frequently encountered in neural networks, and it is often a source of instability and oscillations in a system. It is very important to research the stability of delayed neural network, especially for global asymptotically stability of the neural network with time-varying delay. In the letter, a novel method is proposed in this note for global asymptotically stability of cellular neural networks with time-varying delay. New delay-dependent global asymptotically stability conditions of cellular neural network with time-varying delay is presented by constructing Lyapunov function and using linear matrix inequality (LMI). Finally, numerical examples are given to demonstrate the effect of the proposed method.</abstract><pub>IEEE</pub><doi>10.1109/WCICA.2010.5554634</doi><tpages>6</tpages></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Asymptotic stability Cellular neural networks Delay delayed cellular neural networks (DCNNs) global asymptotically stability LMI Lyapunov functional Numerical stability Stability criteria |
| title | Global asymptotically stability of cellular neural networks with time-varying delay |
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