| 带有变时滞延迟和分布延迟的竞争型神经网络周期解在时间尺度上的的全局指数稳定性 |
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| 引用本文： | 刘洋,杨永清,梁甜,许先云. 带有变时滞延迟和分布延迟的竞争型神经网络周期解在时间尺度上的的全局指数稳定性[J]. 数学研究及应用, 2014, 34(4): 467-474 |
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| 作者姓名： | 刘洋 杨永清 梁甜 许先云 |
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| 作者单位： | 江南大学理学院, 江苏 无锡 214122;江南大学理学院, 江苏 无锡 214122;江南大学理学院, 江苏 无锡 214122;江南大学理学院, 江苏 无锡 214122 |
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| 基金项目： | 中央高校基本科研业务费专项资金(Grant No.JUSRP51317B),国家自然科学基金(Grant No.60875036). |
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| 摘 要： | In this paper, competitive neural networks with time-varying and distributed delays are investigated. By utilizing Lyapunov functional methods, the global exponential stability of periodic solutions of the neural networks is discussed on time scales. In addition, an example is given to illustrate the effectiveness of the theoretical results.
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| 关 键 词： | 全局指数稳定性 竞争神经网络 分布时滞 时间尺度 周期解 Lyapunov泛函方法 时变 |
| 收稿时间： | 2013-05-17 |
| 修稿时间： | 2013-10-12 |
Global Exponential Stability of Periodic Solution for Competitive Neural Networks with Time-Varying and Distributed Delays on Time Scales |
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| Yang LIU,Yongqing YANG,Tian LIANG and Xianyun XU. Global Exponential Stability of Periodic Solution for Competitive Neural Networks with Time-Varying and Distributed Delays on Time Scales[J]. Journal of Mathematical Research with Applications, 2014, 34(4): 467-474 |
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| Authors: | Yang LIU Yongqing YANG Tian LIANG Xianyun XU |
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| Affiliation: | Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Science, Jiangnan University, Jiangsu 214122, P. R. China;Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Science, Jiangnan University, Jiangsu 214122, P. R. China;Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Science, Jiangnan University, Jiangsu 214122, P. R. China;Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Science, Jiangnan University, Jiangsu 214122, P. R. China |
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| Abstract: | In this paper, competitive neural networks with time-varying and distributed delays are investigated. By utilizing Lyapunov functional methods, the global exponential stability of periodic solutions of the neural networks is discussed on time scales. In addition, an example is given to illustrate the effectiveness of the theoretical results. |
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| Keywords: | stability competitive neural networks delays time scales. |
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