一类BAM随机神经网络的稳定性  

Stability of a Class BAM Stochastic Neural Networks

作　　者：缪春芳[1] 柯云泉[1] 

机构地区：[1]绍兴文理学院数学系,浙江绍兴312000

出　　处：《生物数学学报》2008年第4期623-632,共10页Journal of Biomathematics

基　　金：浙江省自然科学基金项目(6040030)

摘　　要：对于一类双向联想记忆(BAM)随机神经网络,研究其全局稳定性和指数稳定性,利用Schwarz积分不等式和It积分性质,给出其稳定性判定的充分性条件.To stochastic neural networks of a class bi-directional associative memory, we investigat their global sability and exponential stability. The general sufficient conditions for global sability and exponential stability of a class BAM stochastic neural networks are established by using the schwarz integral inequality and Ito integral property.

关 键 词：BAM随机神经网络 Schwarz积分不等式 Ito积分性质 全局稳定性 指数稳 定性 

分 类 号：O175.12[理学—数学]