DocumentCode
1652175
Title
Novel Stability Analysis of High-order Cohen-Grossberg Neural Networks with Time-varying Delays
Author
Yan, Ji ; Baotong, Cui
Author_Institution
Southern Yangtze Univ., Wuxi
fYear
2007
Firstpage
176
Lastpage
180
Abstract
This paper addresses global asymptotic stability and global exponential stability for high-order Cohen-Grossberg neural networks with time-varying delays. Some novel global stability criteria of the system is derived by using the method of Lyapunov functions and linear matrix inequality (LMI). An example is given to illustrate the effectiveness of our results.
Keywords
Lyapunov methods; asymptotic stability; delay systems; linear matrix inequalities; neurocontrollers; stability criteria; time-varying systems; Lyapunov functions; global asymptotic stability; global exponential stability; global stability criteria; high-order Cohen-Grossberg neural network; linear matrix inequality; time-varying delay; Associative memory; Asymptotic stability; Communication system control; Control engineering; Delay effects; Electronic mail; Neural networks; Neurons; Stability analysis; Sufficient conditions; High-order Cohen-Grossberg neural networks; Stability; 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.2006.4347370
Filename
4347370
Link To Document