DocumentCode :
3582835
Title :
Asymptotic stability analysis for neural networks with two additive time-varying delays components
Author :
Hao Chen ; Jinxiang Yang
Author_Institution :
Sch. of Math. Sci., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
fYear :
2014
Firstpage :
135
Lastpage :
138
Abstract :
This paper investigates asymptotic stability for delayed neural networks with two additive time-varying delays components. New Lyapunov functional are constructed to derive some new conditions for this system. The proposed result is less conservative because delay decomposition technique and reciprocally convex method are considered.
Keywords :
Lyapunov methods; asymptotic stability; delays; neural nets; Lyapunov functional; additive time-varying delays component; asymptotic stability analysis; delay decomposition technique; neural networks; reciprocally convex method; Additives; Asymptotic stability; Biological neural networks; Delays; Stability criteria; Time-varying systems; Lyapunov functional; Neural networks; delay decomposition technique; reciprocally convex method; time-varying delay;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2014 11th International Computer Conference on
Print_ISBN :
978-1-4799-7207-4
Type :
conf
DOI :
10.1109/ICCWAMTIP.2014.7073377
Filename :
7073377
Link To Document :
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