DocumentCode
2625621
Title
Global asymptotic stability of discrete-time cellular neural networks
Author
Arik, Sabri ; Kilinc, Ali ; Savaci, E. Acar
Author_Institution
Dept. of Electron., Istanbul Univ., Turkey
fYear
1998
fDate
14-17 Apr 1998
Firstpage
52
Lastpage
55
Abstract
This paper presents two sufficient conditions for global stability of discrete-time cellular neural networks (DTCNNs). It is shown that if the first or second norm of the feedback matrix is smaller than one, then a DTCNN converges to a unique and globally asymptotically stable equilibrium point for every external input
Keywords
Lyapunov methods; asymptotic stability; cellular neural nets; convergence; feedback; Lyapunov function; convergence; discrete-time cellular neural networks; equilibrium point; feedback matrix; global asymptotic stability; sufficient conditions; Analog-digital conversion; Asymptotic stability; Cellular neural networks; Digital signal processing; Electronic mail; Integrated circuit modeling; Matrix converters; Neural networks; Neurofeedback; Sufficient conditions;
fLanguage
English
Publisher
ieee
Conference_Titel
Cellular Neural Networks and Their Applications Proceedings, 1998 Fifth IEEE International Workshop on
Conference_Location
London
Print_ISBN
0-7803-4867-2
Type
conf
DOI
10.1109/CNNA.1998.685329
Filename
685329
Link To Document