非线性二元离散神经网络模型的渐近性  

Asymptotic Behaviors for a Class of Nonlinear Discrete-time Neural Networks with Two Neurons

作　　者：刘开宇[1] 王成亮[1] 

机构地区：[1]湖南大学数学与计量经济学院,湖南长沙410082

出　　处：《湖南大学学报（自然科学版）》2010年第3期53-56,共4页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(10971057)

摘　　要：针对一类具M-P型非线性离散神经网络模型,提出了当初值设定在振动型函数空间上时解的渐近行为问题.通过构造解的表达式,并利用不等式技巧,结合分析方法,获得了大阈值情形下系统唯一平衡点全局稳定性,进一步建立了临界阈值情形下初值不同的解趋于不同平衡点的充要条件.所得结果解决了文献中的相关问题.The asymptotic behavior problem for a class of nonlinear discrete-time neural network model with oscillation initial function was proposed. By making the expression of solutions, global exponential asymptotic stability of the unique equilibrium was obtained if the absolute values of threshold values are large, the necessary and sufficient condictions which guarantee the solutions of the model tend to one of those equilibria respectively in the critical case were established . These results have solved relevant problems in the references.

关 键 词：神经网络 非线性系统 渐近性 

分 类 号：O175.13[理学—数学]