Multiperiodicity and Attractivity Analysis for a Class of High-order Cohen-Grossberg Neural Networks

被引:0
|
作者
Sheng, Li [1 ]
Gao, Ming [2 ]
机构
[1] East China Univ Petr, Coll Informat & Control Engn, Qingdao 266580, Peoples R China
[2] Shandong Univ Sci & Technol, Coll Informat & Elect Engn, Qingdao 266590, Peoples R China
基金
高等学校博士学科点专项科研基金;
关键词
Multiperiodicity; Multistability; High-order Cohen-Grossberg neural networks; Exponentially attractive; MULTISTABILITY;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, the multiperiodicity of a class of high-order Cohen-Grossberg neural networks (HOCGNNs) with special activation functions is discussed by using analysis approach and decomposition of state space. The activation functions of this class of neural networks consist of nondecreasing functions with saturation, standard activation functions of cellular neural networks, etc. It is shown that the n-neuron HOCGNNs can have 2(n) locally exponentially attractive periodic orbits located in saturation regions. In addition, a condition is derived for ascertaining the periodic orbit to be locally exponentially attractive and to be located in any designated region. Finally, an example is given to show the effectiveness of the obtained results.
引用
收藏
页码:1489 / 1494
页数:6
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