[1] Liang Jinling,. Global exponential periodicityof a class of impulsive neural networks [J]. Journal of Southeast University (English Edition), 2005, 21 (4): 509-512. [doi:10.3969/j.issn.1003-7985.2005.04.027]
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Global exponential periodicityof a class of impulsive neural networks(
)
一类脉冲神经网络的全局指数周期解
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 21
- Issue:
- 2005 4
- Page:
- 509-512
- Research Field:
- Mathematics, Physics, Mechanics
- Publishing date:
- 2005-12-30
Info
- Title:
- Global exponential periodicityof a class of impulsive neural networks
- 一类脉冲神经网络的全局指数周期解
- Author(s):
- Liang Jinling
- Department of Mathematics, Southeast University, Nanjing 210096, China
- 梁金玲
- 东南大学数学系, 南京 210096
- Keywords:
- global exponential periodicity; impulsive neural networks; Lyapunov function; Lipschitz activation function
- PACS:
- O175.12
- DOI:
- 10.3969/j.issn.1003-7985.2005.04.027
- Abstract:
- By the Lyapunov function method, combined with the inequality techniques, some criteria are established to ensure the existence, uniqueness and global exponential stability of the periodic solution for a class of impulsive neural networks.The results obtained only require the activation functions to be globally Lipschitz continuous without assuming their boundedness, monotonicity or differentiability.The conditions are easy to check in practice and they can be applied to design globally exponentially periodic impulsive neural networks.
- 利用Lyapunov函数并结合不等式的技巧, 给出了一些充分判据来确保一类脉冲神经网络系统具有全局指数稳定性的周期解.给出的充分判据仅仅要求激励函数是李普希兹连续的, 而对它的有界性、单调性及可微性都不再要求.这些充分判据在实际应用中非常容易验证, 也可利用这些判据来设计全局指数周期的脉冲神经网络.
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Memo
- Memo:
- Biography: Liang Jinling(1974—), female, lecturer, jinlliang@seu.edu.cn.