高阶变时滞广义细胞神经网络的全局指数周期性  

Exponential Periodicity and Stability of High-order Generalized Cellular Neural Networks with Time-varying Delays

作　　者：周立群[1] 翁良燕[1] 

机构地区：[1]天津师范大学数学科学学院,天津300387

出　　处：《数学的实践与认识》2013年第14期271-279,共9页Mathematics in Practice and Theory

基　　金：国家自然科学基金(60974144);天津高等学校科技发展基金(20100813);天津师范大学博士基金项目(52LX34)

摘　　要：研究了高阶变时滞广义细胞神经网络的全局指数周期性.引入可调参数和通过构造合适的Lyapunov泛函并利用Brouwer压缩映象原理,得到了神经网络周期解存在唯一且全局指数周期与全局指数稳定的充分条件,并给出一个例子说明结论的有效性.Global exponential periodicity and stability of high-order generalized cellular neural networks with time-varying delays is investigated. By ingeniously importing param- eters and constructing suitable Lyapunov functional and applying the Brouwer contraction mapping principle, a sufficient condition ensuring existence and uniqueness of periodic solu- tion and global exponential periodicity and global exponential stability is obtained. And an example is given to illustrate the effectiveness of the obtained results.

关 键 词：广义细胞神经网络 时变时滞 全局指数稳定性 周期解 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]