多时变时滞细胞神经网络的全局指数稳定性  

Globally Exponential Stability of Cellular Neural Networks with Multiple Time-Varying Delays

作　　者：王占山[1] 张化光[1] 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110004

出　　处：《东北大学学报（自然科学版）》2006年第4期367-370,共4页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(6024401760325311);辽宁省自然科学基金资助项目(20022030)

摘　　要：针对一类多时变时滞细胞神经网络,利用Young不等式和Halanay不等式技术,给出了保证平衡点惟一性和全局指数稳定性的几个充分判据.所得到的全局指数稳定判据完全独立于时滞,不要求时变时滞的可微性和神经元激励函数的严格单调性,且通过几个注释说明本文的结果改进和扩展了现有一些文献中的结果.仿真例子证明了本文结果的有效性.Globally exponential stability of a class of cellular neural networks with multiple time varying delays is investigated. Using the technique by virtue of Young and Halanay inequalities, some new sufficient criteria are given to ensure the uniqueness of equilibrium point and globally exponential stability. In this way the criteria given for globally exponential stability are entirely independent of time delay without the differentiability of time-varying delay and the strict monotonicity of neuron＇s excitation function, In addition, some remarks are given to explain how the results as shown in this paper improves and extends the earlier works as references of which the effectivencess is proved via simulation example.

关 键 词：全局指数稳定 细胞神经网络 多时变时滞 LYAPUNOV函数 YOUNG不等式 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]