Improved stochastic dissipativity of uncertain discrete-time neural networks with multiple delays and impulses
This paper investigates the problem of global dissipativity and global exponential dissipativity for a class of uncertain discrete-time stochastic neural networks with multiple time-varying delays. Here the multiple time-varying delays are assumed to be discrete and distributed and the uncertainties...
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| Veröffentlicht in: | International journal of machine learning and cybernetics 2015-04, Vol.6 (2), p.289-305 |
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| container_title | International journal of machine learning and cybernetics |
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| creator | Raja, R. Karthik Raja, U. Samidurai, R. Leelamani, A. |
| description | This paper investigates the problem of global dissipativity and global exponential dissipativity for a class of uncertain discrete-time stochastic neural networks with multiple time-varying delays. Here the multiple time-varying delays are assumed to be discrete and distributed and the uncertainties are assumed to be time-varying norm-bounded parameter uncertainties. By choosing a novel Lyapunov functional, combining with linear matrix inequality technique (LMI), Jensen’s inequality and stochastic analysis method, a new delay-dependent global dissipativity criterion is obtained in the form of LMI, which can be easily verified numerically using the effective LMI toolbox in Matlab. One important feature presents in our paper is that without employing model transformation and free weighting matrices our obtained result leads to less conservatism. Two illustrative examples are given to show the usefulness of the obtained dissipativity conditions. |
| doi_str_mv | 10.1007/s13042-013-0215-z |
| format | Article |
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Here the multiple time-varying delays are assumed to be discrete and distributed and the uncertainties are assumed to be time-varying norm-bounded parameter uncertainties. By choosing a novel Lyapunov functional, combining with linear matrix inequality technique (LMI), Jensen’s inequality and stochastic analysis method, a new delay-dependent global dissipativity criterion is obtained in the form of LMI, which can be easily verified numerically using the effective LMI toolbox in Matlab. One important feature presents in our paper is that without employing model transformation and free weighting matrices our obtained result leads to less conservatism. 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J. Mach. Learn. & Cyber</addtitle><description>This paper investigates the problem of global dissipativity and global exponential dissipativity for a class of uncertain discrete-time stochastic neural networks with multiple time-varying delays. Here the multiple time-varying delays are assumed to be discrete and distributed and the uncertainties are assumed to be time-varying norm-bounded parameter uncertainties. By choosing a novel Lyapunov functional, combining with linear matrix inequality technique (LMI), Jensen’s inequality and stochastic analysis method, a new delay-dependent global dissipativity criterion is obtained in the form of LMI, which can be easily verified numerically using the effective LMI toolbox in Matlab. One important feature presents in our paper is that without employing model transformation and free weighting matrices our obtained result leads to less conservatism. Two illustrative examples are given to show the usefulness of the obtained dissipativity conditions.</description><subject>Artificial Intelligence</subject><subject>Complex Systems</subject><subject>Computational Intelligence</subject><subject>Control</subject><subject>Dissipation</subject><subject>Engineering</subject><subject>Equilibrium</subject><subject>Euclidean space</subject><subject>Linear matrix inequalities</subject><subject>Mechatronics</subject><subject>Neural networks</subject><subject>Numbers</subject><subject>Original Article</subject><subject>Parameter uncertainty</subject><subject>Pattern Recognition</subject><subject>Robotics</subject><subject>Systems Biology</subject><issn>1868-8071</issn><issn>1868-808X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1UE1LAzEUXETBUvsDvAU8R19edje7Ryl-FApeFLyFbDa1qftlkm1pf70pFT35LjPwZubxJkmuGdwyAHHnGYcUKTBOAVlGD2fJhBV5QQso3s9_uWCXycz7DcTJgXPASdIt2sH1W1MTH3q9Vj5YTWrrvR1UsFsb9qRfkbHTxgVlu-NKOxMMDbY1pDOjU02EsOvdpyc7G9akHZtgh8aQ2jRq74nqamLbYWy88VfJxUpFMvvBafL2-PA6f6bLl6fF_H5JNWd5oBlWrC5SUCpPDWqeZwKw1CwXwFFXZYWIgpU1oqp0BZCVzGihRS7qLMcS-DS5OeXG575G44Pc9KPr4kmJJQKmGRdpVLGTSrvee2dWcnC2VW4vGchjs_LUrIzNymOz8hA9ePL4qO0-jPtL_t_0DSqdfXk</recordid><startdate>20150401</startdate><enddate>20150401</enddate><creator>Raja, R.</creator><creator>Karthik Raja, U.</creator><creator>Samidurai, R.</creator><creator>Leelamani, A.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope></search><sort><creationdate>20150401</creationdate><title>Improved stochastic dissipativity of uncertain discrete-time neural networks with multiple delays and impulses</title><author>Raja, R. ; Karthik Raja, U. ; Samidurai, R. ; Leelamani, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-52b1d840aa64e2c3657029c167032cb9b222719d22abcb00591ec7c767d562903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Artificial Intelligence</topic><topic>Complex Systems</topic><topic>Computational Intelligence</topic><topic>Control</topic><topic>Dissipation</topic><topic>Engineering</topic><topic>Equilibrium</topic><topic>Euclidean space</topic><topic>Linear matrix inequalities</topic><topic>Mechatronics</topic><topic>Neural networks</topic><topic>Numbers</topic><topic>Original Article</topic><topic>Parameter uncertainty</topic><topic>Pattern Recognition</topic><topic>Robotics</topic><topic>Systems Biology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Raja, R.</creatorcontrib><creatorcontrib>Karthik Raja, U.</creatorcontrib><creatorcontrib>Samidurai, R.</creatorcontrib><creatorcontrib>Leelamani, A.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><jtitle>International journal of machine learning and cybernetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Raja, R.</au><au>Karthik Raja, U.</au><au>Samidurai, R.</au><au>Leelamani, A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improved stochastic dissipativity of uncertain discrete-time neural networks with multiple delays and impulses</atitle><jtitle>International journal of machine learning and cybernetics</jtitle><stitle>Int. 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| subjects | Artificial Intelligence Complex Systems Computational Intelligence Control Dissipation Engineering Equilibrium Euclidean space Linear matrix inequalities Mechatronics Neural networks Numbers Original Article Parameter uncertainty Pattern Recognition Robotics Systems Biology |
| title | Improved stochastic dissipativity of uncertain discrete-time neural networks with multiple delays and impulses |
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