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
Link To Document :
بازگشت