变时滞BAM神经网络的全局指数稳定性  

Global Exponential Stability of BAM Neural Networks with Variable Delays

作　　者：张丽娟[1] 时宝[1] 

机构地区：[1]海军航空工程学院应用数学研究所

出　　处：《数学的实践与认识》2008年第5期148-153,共6页Mathematics in Practice and Theory

基　　金：国家自然科学基金(10461006);烟台大学青年基金(SX06Z9)

摘　　要：研究一类变时滞BAM神经网络平衡点的全局指数稳定性问题.在不要求激励函数全局Lipschitz条件下,通过构造合适的Lyapunov泛函,并结合Young不等式,得到了BAM神经网络模型在一定条件下全局指数稳定的一些充分条件,推广和改进了前人的相关结论,为综合设计指数稳定的时滞BAM神经网络提供了依据.The main purpose of this paper is to study the global exponential stability of the equilibrium point of a class of BAM neural networks with variable delays. Without assuming global Lipschitz conditions on the activation functions, by constructing suitable Lyapunov functional and the Young inequality technique, Some sufficient conditions are derived for the global exponential stability of BAM neural networks. We extend and improve previously known results. These can be applied to design the global exponentially stable delayed BAM networks.

关 键 词：BAM神经网络 变时滞 全局指数稳定性 

分 类 号：O175[理学—数学]