Combined Convex Technique on Delay-Distribution-Dependent Stability for Delayed Neural Networks
Together with the Lyapunov-Krasovskii functional approach and an improved delay-partitioning idea, one novel sufficient condition is derived to guarantee a class of delayed neural networks to be asymptotically stable in the mean-square sense, in which the probabilistic variable delay and both of del...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2012/426350 |
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| _version_ | 1849468140940951552 |
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| author | Ting Wang Tao Li Mingxiang Xue Shumin Fei |
| author_facet | Ting Wang Tao Li Mingxiang Xue Shumin Fei |
| author_sort | Ting Wang |
| collection | DOAJ |
| description | Together with the Lyapunov-Krasovskii functional approach and an improved delay-partitioning idea, one novel sufficient condition is derived to guarantee a class of delayed neural networks to be asymptotically stable in the mean-square sense, in which the probabilistic variable delay and both of delay variation limits can be measured. Through combining the reciprocal convex technique and convex technique one, the criterion is presented via LMIs and its solvability heavily depends on the sizes of both time-delay range and its variations, which can become much less conservative than those present ones by thinning the delay intervals. Finally, it can be demonstrated by four numerical examples that our idea reduces the conservatism more effectively than some earlier reported ones. |
| format | Article |
| id | doaj-art-fb39486f8527464fb1d41d9f64025bda |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-fb39486f8527464fb1d41d9f64025bda2025-08-20T03:25:56ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2012-01-01201210.1155/2012/426350426350Combined Convex Technique on Delay-Distribution-Dependent Stability for Delayed Neural NetworksTing Wang0Tao Li1Mingxiang Xue2Shumin Fei3Key Laboratory of Measurement and Control of CSE, School of Automation, Southeast University, Ministry of Education, Nanjing 210096, ChinaKey Laboratory of Measurement and Control of CSE, School of Automation, Southeast University, Ministry of Education, Nanjing 210096, ChinaKey Laboratory of Measurement and Control of CSE, School of Automation, Southeast University, Ministry of Education, Nanjing 210096, ChinaKey Laboratory of Measurement and Control of CSE, School of Automation, Southeast University, Ministry of Education, Nanjing 210096, ChinaTogether with the Lyapunov-Krasovskii functional approach and an improved delay-partitioning idea, one novel sufficient condition is derived to guarantee a class of delayed neural networks to be asymptotically stable in the mean-square sense, in which the probabilistic variable delay and both of delay variation limits can be measured. Through combining the reciprocal convex technique and convex technique one, the criterion is presented via LMIs and its solvability heavily depends on the sizes of both time-delay range and its variations, which can become much less conservative than those present ones by thinning the delay intervals. Finally, it can be demonstrated by four numerical examples that our idea reduces the conservatism more effectively than some earlier reported ones.http://dx.doi.org/10.1155/2012/426350 |
| spellingShingle | Ting Wang Tao Li Mingxiang Xue Shumin Fei Combined Convex Technique on Delay-Distribution-Dependent Stability for Delayed Neural Networks Discrete Dynamics in Nature and Society |
| title | Combined Convex Technique on Delay-Distribution-Dependent Stability for Delayed Neural Networks |
| title_full | Combined Convex Technique on Delay-Distribution-Dependent Stability for Delayed Neural Networks |
| title_fullStr | Combined Convex Technique on Delay-Distribution-Dependent Stability for Delayed Neural Networks |
| title_full_unstemmed | Combined Convex Technique on Delay-Distribution-Dependent Stability for Delayed Neural Networks |
| title_short | Combined Convex Technique on Delay-Distribution-Dependent Stability for Delayed Neural Networks |
| title_sort | combined convex technique on delay distribution dependent stability for delayed neural networks |
| url | http://dx.doi.org/10.1155/2012/426350 |
| work_keys_str_mv | AT tingwang combinedconvextechniqueondelaydistributiondependentstabilityfordelayedneuralnetworks AT taoli combinedconvextechniqueondelaydistributiondependentstabilityfordelayedneuralnetworks AT mingxiangxue combinedconvextechniqueondelaydistributiondependentstabilityfordelayedneuralnetworks AT shuminfei combinedconvextechniqueondelaydistributiondependentstabilityfordelayedneuralnetworks |