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H Infinity Control of Switching Delayed Stochastic Hopfield Neural Network Systems with Markovian Jumping Parameters
This paper considers the problem of H infin control of a class of Hopfield neural network systems with time-varying delays and Markovian jumping parameters. The delays are assumed to be bounded. The jumping parameters considered here are generated from a continuous-time discrete-state homogenous Mar...
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| creator | Xiaole Xu Lixin Gao Haiwa Guan |
| description | This paper considers the problem of H infin control of a class of Hopfield neural network systems with time-varying delays and Markovian jumping parameters. The delays are assumed to be bounded. The jumping parameters considered here are generated from a continuous-time discrete-state homogenous Markov process, which are governed by a Markov process with discrete and finite state space. Our purpose is to design state feedback controllers that guarantee the systems are stochastic stability with a prescribed performance. Based on the Lyapunov method and stochastic analysis approach, a sufficient condition for the solvability of the problems are derived in term of linear matrix inequalities, which can be easily checked by resorting to available software packages. A numerical example is exploited to demonstrate the effectiveness of the proposed results. |
| doi_str_mv | 10.1109/ICICTA.2008.386 |
| format | conference_proceeding |
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The delays are assumed to be bounded. The jumping parameters considered here are generated from a continuous-time discrete-state homogenous Markov process, which are governed by a Markov process with discrete and finite state space. Our purpose is to design state feedback controllers that guarantee the systems are stochastic stability with a prescribed performance. Based on the Lyapunov method and stochastic analysis approach, a sufficient condition for the solvability of the problems are derived in term of linear matrix inequalities, which can be easily checked by resorting to available software packages. A numerical example is exploited to demonstrate the effectiveness of the proposed results.</description><identifier>ISBN: 0769533574</identifier><identifier>ISBN: 9780769533575</identifier><identifier>DOI: 10.1109/ICICTA.2008.386</identifier><identifier>LCCN: 2008930493</identifier><language>eng</language><publisher>IEEE</publisher><subject>Control systems ; delay ; Delay systems ; H infinity control ; Hopfield neural networks ; Markov processes ; Markovian ; neural network ; Stability ; State feedback ; State-space methods ; Stochastic systems ; Switching systems ; Time varying systems</subject><ispartof>2008 International Conference on Intelligent Computation Technology and Automation (ICICTA), 2008, Vol.1, p.380-384</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/4659510$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4659510$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Xiaole Xu</creatorcontrib><creatorcontrib>Lixin Gao</creatorcontrib><creatorcontrib>Haiwa Guan</creatorcontrib><title>H Infinity Control of Switching Delayed Stochastic Hopfield Neural Network Systems with Markovian Jumping Parameters</title><title>2008 International Conference on Intelligent Computation Technology and Automation (ICICTA)</title><addtitle>ICICTA</addtitle><description>This paper considers the problem of H infin control of a class of Hopfield neural network systems with time-varying delays and Markovian jumping parameters. The delays are assumed to be bounded. The jumping parameters considered here are generated from a continuous-time discrete-state homogenous Markov process, which are governed by a Markov process with discrete and finite state space. Our purpose is to design state feedback controllers that guarantee the systems are stochastic stability with a prescribed performance. Based on the Lyapunov method and stochastic analysis approach, a sufficient condition for the solvability of the problems are derived in term of linear matrix inequalities, which can be easily checked by resorting to available software packages. A numerical example is exploited to demonstrate the effectiveness of the proposed results.</description><subject>Control systems</subject><subject>delay</subject><subject>Delay systems</subject><subject>H infinity control</subject><subject>Hopfield neural networks</subject><subject>Markov processes</subject><subject>Markovian</subject><subject>neural network</subject><subject>Stability</subject><subject>State feedback</subject><subject>State-space methods</subject><subject>Stochastic systems</subject><subject>Switching systems</subject><subject>Time varying systems</subject><isbn>0769533574</isbn><isbn>9780769533575</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotjElrAjEYQANFaLWee-glf0D7ZZtJjjJdnGIX0J4lJpmaOoskUZl_X6U9PXjwHkJ3BKaEgHooi7JYzaYUQE6ZzK7QEPJMCcZEzgdoePGKAVfsGo1j_AEAorKcUHKD0hyXbeVbn3pcdG0KXY27Ci9PPpmtb7_xo6t17yxeps5sdUze4Hm3r7yrLX53h6DrM9KpCzu87GNyTcTndovfdNh1R69b_Hpo9pfTpw66ccmFeIsGla6jG_9zhL6en1bFfLL4eCmL2WLiCYg0kS4nLBcGKgZC5YpLySWrQNLM5hvKjRbcCKDSSMeAqo21wlonLN9QwnnFRuj-7-udc-t98I0O_ZpnQgkC7BchuFwm</recordid><startdate>200810</startdate><enddate>200810</enddate><creator>Xiaole Xu</creator><creator>Lixin Gao</creator><creator>Haiwa Guan</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200810</creationdate><title>H Infinity Control of Switching Delayed Stochastic Hopfield Neural Network Systems with Markovian Jumping Parameters</title><author>Xiaole Xu ; Lixin Gao ; Haiwa Guan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i105t-8e71375c0f305979488483f0826d7b24ca54c5028c8e3029bdd5dde5d4b2144f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Control systems</topic><topic>delay</topic><topic>Delay systems</topic><topic>H infinity control</topic><topic>Hopfield neural networks</topic><topic>Markov processes</topic><topic>Markovian</topic><topic>neural network</topic><topic>Stability</topic><topic>State feedback</topic><topic>State-space methods</topic><topic>Stochastic systems</topic><topic>Switching systems</topic><topic>Time varying systems</topic><toplevel>online_resources</toplevel><creatorcontrib>Xiaole Xu</creatorcontrib><creatorcontrib>Lixin Gao</creatorcontrib><creatorcontrib>Haiwa Guan</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/IET 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>Xiaole Xu</au><au>Lixin Gao</au><au>Haiwa Guan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>H Infinity Control of Switching Delayed Stochastic Hopfield Neural Network Systems with Markovian Jumping Parameters</atitle><btitle>2008 International Conference on Intelligent Computation Technology and Automation (ICICTA)</btitle><stitle>ICICTA</stitle><date>2008-10</date><risdate>2008</risdate><volume>1</volume><spage>380</spage><epage>384</epage><pages>380-384</pages><isbn>0769533574</isbn><isbn>9780769533575</isbn><abstract>This paper considers the problem of H infin control of a class of Hopfield neural network systems with time-varying delays and Markovian jumping parameters. The delays are assumed to be bounded. The jumping parameters considered here are generated from a continuous-time discrete-state homogenous Markov process, which are governed by a Markov process with discrete and finite state space. Our purpose is to design state feedback controllers that guarantee the systems are stochastic stability with a prescribed performance. Based on the Lyapunov method and stochastic analysis approach, a sufficient condition for the solvability of the problems are derived in term of linear matrix inequalities, which can be easily checked by resorting to available software packages. A numerical example is exploited to demonstrate the effectiveness of the proposed results.</abstract><pub>IEEE</pub><doi>10.1109/ICICTA.2008.386</doi><tpages>5</tpages></addata></record> |
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| identifier | ISBN: 0769533574 |
| ispartof | 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA), 2008, Vol.1, p.380-384 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Control systems delay Delay systems H infinity control Hopfield neural networks Markov processes Markovian neural network Stability State feedback State-space methods Stochastic systems Switching systems Time varying systems |
| title | H Infinity Control of Switching Delayed Stochastic Hopfield Neural Network Systems with Markovian Jumping Parameters |
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