DocumentCode
1583560
Title
Globally Exponential Stability of Discrete-time Cellular Neural Networks With Discrete Delays
Author
Ju, Peijun ; Zhang, Wei ; Liu, Guocai ; Tian, Li
Author_Institution
Taishan Univ., Taian
Volume
1
fYear
2007
Firstpage
188
Lastpage
191
Abstract
Using the technique by virtue of Young and Halanay inequalities, a new sufficient condition for the globally exponential stability of a class of discrete-time cellular neural networks with delays is given. We discard the demand that activation functions must be derivable and only request them to be Lipschitz continuous, in this way the criteria given for globally exponential stability relies on the feedback matrices and is independent of the delay parameter.
Keywords
asymptotic stability; cellular neural nets; delays; discrete time systems; feedback; matrix algebra; neurocontrollers; transfer functions; Lipschitz continuous function; activation function; discrete delay; discrete-time cellular neural network; feedback matrix; globally exponential stability; Artificial neural networks; Cellular neural networks; Delay effects; Delay systems; Differential equations; Hydrogen; Mathematics; Neurofeedback; Stability criteria; Sufficient conditions;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location
Haikou
Print_ISBN
978-0-7695-2875-5
Type
conf
DOI
10.1109/ICNC.2007.406
Filename
4344179
Link To Document