Global Exponential Stability of Antiperiodic Solutions for Discrete-Time Neural Networks with Mixed Delays and Impulses
The problem on global exponential stability of antiperiodic solution is investigated for a class of impulsive discrete-time neural networks with time-varying discrete delays and distributed delays. By constructing an appropriate Lyapunov-Krasovskii functional, and using the contraction mapping princ...
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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/10.1155/2012/168375 |
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| _version_ | 1832555536472604672 |
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| author | Xiaofeng Chen Qiankun Song |
| author_facet | Xiaofeng Chen Qiankun Song |
| author_sort | Xiaofeng Chen |
| collection | DOAJ |
| description | The problem on global exponential stability of antiperiodic solution is investigated for a class of impulsive discrete-time neural networks with time-varying discrete delays and distributed delays. By constructing an appropriate Lyapunov-Krasovskii functional, and using the contraction mapping principle and the matrix inequality techniques, a new delay-dependent criterion for checking the existence, uniqueness, and global exponential stability of anti-periodic solution is derived in linear matrix inequalities (LMIs). Two simulation examples are given to show the effectiveness of the proposed result. |
| format | Article |
| id | doaj-art-65678b4c9d3e4d3b904bd8af9e3ed505 |
| 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-65678b4c9d3e4d3b904bd8af9e3ed5052025-02-03T05:47:51ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2012-01-01201210.1155/2012/168375168375Global Exponential Stability of Antiperiodic Solutions for Discrete-Time Neural Networks with Mixed Delays and ImpulsesXiaofeng Chen0Qiankun Song1Department of Mathematics, Chongqing Jiaotong University, Chongqing 400074, ChinaDepartment of Mathematics, Chongqing Jiaotong University, Chongqing 400074, ChinaThe problem on global exponential stability of antiperiodic solution is investigated for a class of impulsive discrete-time neural networks with time-varying discrete delays and distributed delays. By constructing an appropriate Lyapunov-Krasovskii functional, and using the contraction mapping principle and the matrix inequality techniques, a new delay-dependent criterion for checking the existence, uniqueness, and global exponential stability of anti-periodic solution is derived in linear matrix inequalities (LMIs). Two simulation examples are given to show the effectiveness of the proposed result.http://dx.doi.org/10.1155/2012/168375 |
| spellingShingle | Xiaofeng Chen Qiankun Song Global Exponential Stability of Antiperiodic Solutions for Discrete-Time Neural Networks with Mixed Delays and Impulses Discrete Dynamics in Nature and Society |
| title | Global Exponential Stability of Antiperiodic Solutions for Discrete-Time Neural Networks with Mixed Delays and Impulses |
| title_full | Global Exponential Stability of Antiperiodic Solutions for Discrete-Time Neural Networks with Mixed Delays and Impulses |
| title_fullStr | Global Exponential Stability of Antiperiodic Solutions for Discrete-Time Neural Networks with Mixed Delays and Impulses |
| title_full_unstemmed | Global Exponential Stability of Antiperiodic Solutions for Discrete-Time Neural Networks with Mixed Delays and Impulses |
| title_short | Global Exponential Stability of Antiperiodic Solutions for Discrete-Time Neural Networks with Mixed Delays and Impulses |
| title_sort | global exponential stability of antiperiodic solutions for discrete time neural networks with mixed delays and impulses |
| url | http://dx.doi.org/10.1155/2012/168375 |
| work_keys_str_mv | AT xiaofengchen globalexponentialstabilityofantiperiodicsolutionsfordiscretetimeneuralnetworkswithmixeddelaysandimpulses AT qiankunsong globalexponentialstabilityofantiperiodicsolutionsfordiscretetimeneuralnetworkswithmixeddelaysandimpulses |