DocumentCode
2899285
Title
On Global Asymptotic Stability of a Class of Neural Networks with Time Delays
Author
Shao, Jin-Liang ; Huang, Ting-Zhu
Author_Institution
Sch. of Appl. Math., Univ. of Electron. Sci. & Technol. of China, Chengdu
fYear
2006
fDate
13-16 Aug. 2006
Firstpage
4120
Lastpage
4123
Abstract
In this paper, a class of Hopfield neural networks with distributed time delays is investigated. Based on globally Lipschitz continuous activation function and M-matrix theory, a proper Lyapunov function is constructed and employed to present a sufficient condition for the uniqueness and global asymptotic stability of the equilibrium point, and the result is independent of the delay parameter
Keywords
Hopfield neural nets; Lyapunov matrix equations; asymptotic stability; delays; transfer functions; Hopfield neural networks; Lipschitz continuous activation function; Lyapunov function; M-matrix theory; distributed time delays; global asymptotic stability; Associative memory; Asymptotic stability; Cybernetics; Delay effects; Electronic mail; Hopfield neural networks; Lyapunov method; Machine learning; Mathematics; Neural networks; Pattern recognition; Sufficient conditions; Delayed neural networks; M-matrix; global asymptotic stability;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location
Dalian, China
Print_ISBN
1-4244-0061-9
Type
conf
DOI
10.1109/ICMLC.2006.258872
Filename
4028793
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