具离散时滞的混合双向联想记忆神经网络的周期解  

Periodic solution of hybrid bidirectional associative memory neural networks with discrete delays

作　　者：谢惠琴[1] 王全义[2] 

机构地区：[1]福州大学数学与计算机科学学院,福建福州350002 [2]华侨大学数学系,福建泉州362021

出　　处：《福州大学学报（自然科学版）》2008年第4期469-474,共6页Journal of Fuzhou University(Natural Science Edition)

基　　金：福州大学科技发展基金资助项目(0030824775)

摘　　要：在放弃活跃函数的有界性、单调递增性和可微性条件下,引入可调实参数di>0(i=1,2,…,n),通过构造Lyapunov泛函和运用不等式分析技巧,对一类具离散时滞的混合双向联想记忆神经网络的周期解、平衡点的存在唯一性和全局指数稳定性给出了一些新的充分准则.Without assuming the boundedness, monotonicity and differentiability of the activation functions, by ingeniously importing real parameters di 〉 0 （i = 1, 2,…, n） which can be adjusted, making use of the Lyapunov functional method and some analysis techniques, some new sufficient conditions for the existence, uniqueness, and the global exponential stability of the periodic solution and equilibrium of hybrid bidirectional associative memory neural networks with discrete delays are established.

关 键 词：双向联想记忆神经网络 LYAPUNOV泛函 周期解 全局指数稳定性 

分 类 号：O175.1[理学—数学]