DocumentCode
2292754
Title
Multiperiodicity and attractivity analysis for a class of high-order Cohen-Grossberg neural networks
Author
Sheng, Li ; Gao, Ming
Author_Institution
Coll. of Inf. & Control Eng., China Univ. of Pet. (East China), Qingdao, China
fYear
2012
fDate
6-8 July 2012
Firstpage
1489
Lastpage
1494
Abstract
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 2n 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.
Keywords
cellular neural nets; state-space methods; transfer functions; attractivity analysis; cellular neural networks; high-order Cohen-Grossberg neural networks; multiperiodicity analysis; n-neuron HOCGNN; periodic orbit; saturation regions; standard activation functions; state space decomposition; Biological neural networks; Educational institutions; Limit-cycles; Orbits; Space vehicles; Vectors; Exponentially attractive; High-order Cohen-Grossberg neural networks; Multiperiodicity; Multistability;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation (WCICA), 2012 10th World Congress on
Conference_Location
Beijing
Print_ISBN
978-1-4673-1397-1
Type
conf
DOI
10.1109/WCICA.2012.6358114
Filename
6358114
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