变时滞二阶Cohen-Grossberg BAM神经网络周期解的全局指数稳定性  

Global Exponential Stability of Periodic Solution to Second-order Cohen-Grossberg BAM Neural Networks with Time-varying Delays

作　　者：廖华英[1] 何西兵 徐向阳[1] LIAO Huan-ying;HE Xi-bing;XU Xiang-yang(Department of Mathematics and Computer Science,Nanchang Normal University,Nanchang 330032,China)

机构地区：[1]南昌师范学院数学与计算机科学系,江西南昌330032

出　　处：《南昌师范学院学报》2018年第3期1-3,共3页Journal of Nanchang Normal University

基　　金：江西省教育厅科学技术研究项目青年项目"非线性双调和方程解的性质";编号:GJJ151250;南昌师范学院校级项目"对几类时滞泛函微分方程动力学性质的研究";编号:18KJZD01

摘　　要：文章通过构造Lyapunov函数,利用周期解的存在性,讨论了变时滞二阶Cohen-Grossberg BAM神经网络周期解的全局指数稳定性。并举出一个例子来验证我们的结论是有效的。In this paper,using the existence result of periodic solution and constructing a Lyapunov functional,we discuss the global exponential stability of periodic solution for the second-order Cohen-Grossberg BAM neural networks with timevarying delays. And we give an example to demon-strate the validity of our global exponential stability result of the periodic solution for the above neural network.

关 键 词：周期解 COHEN-GROSSBERG BAM神经网络 全局指数稳定性 LYAPUNOV函数 

分 类 号：O175.7[理学—数学]