DocumentCode
1639320
Title
Exponential Stability of Delayed High-order Hopfield-type Neural Networks with Diffusion
Author
Xuyang, Lou ; Baotong, Cui
Author_Institution
Southern Yangtze Univ., Wuxi
fYear
2007
Firstpage
83
Lastpage
86
Abstract
This paper considers a generalized model of high-order Hopfield-type neural networks with time-varying delays and reaction-diffusion terms. By using the method of Lyapunov function and Halanay´s inequality, we investigate the global exponential stability of high-order Hopfield-type neural networks with time-varying delays and reaction-diffusion terms. A sufficient condition for ensuring global exponential stability of these networks is derived, and the estimated exponential convergence rate is also obtained. As an illustration, an numerical example is worked out using the results obtained.
Keywords
Hopfield neural nets; Lyapunov methods; asymptotic stability; delays; reaction-diffusion systems; Lyapunov function; delayed high-order Hopfield-type neural networks; global exponential stability; inequality; reaction-diffusion; time-varying delays; Convergence; Delay effects; Electronic mail; Hopfield neural networks; Lyapunov method; Neural networks; Neurons; Stability; Sufficient conditions; Symmetric matrices; Exponential stability; Lyapunov function; Neural networks; Reaction-diffusion terms; Time-varying delays;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference, 2007. CCC 2007. Chinese
Conference_Location
Hunan
Print_ISBN
978-7-81124-055-9
Electronic_ISBN
978-7-900719-22-5
Type
conf
DOI
10.1109/CHICC.2006.4346841
Filename
4346841
Link To Document