DocumentCode :
3215388
Title :
Globally asymptotic stability for neural networks with time-varying delay via delay-decomposition approach
Author :
Shen-Ping Xiao ; Lin-Xing Xu ; Gang Chen ; Ling-Shuang Kong
Author_Institution :
Sch. of Electr. & Inf. Eng., Hunan Univ. of Technol., Zhuzhou, China
fYear :
2015
fDate :
23-25 May 2015
Firstpage :
2309
Lastpage :
2313
Abstract :
In this article, the problem of the stability for delay-dependent neural networks is concerned. A new Lyapunov-Krasovskii functional is introduced, so as to obtain some more superior delay-dependent stability criterion. Moreover, by employing the delay decomposition technique, some novel absolute stability conditions are established, which refine and improve some existing ones. Finally, the feasibility and superiority of the proposed method is demonstrated by a numerical example.
Keywords :
Lyapunov methods; asymptotic stability; delays; neural nets; Lyapunov-Krasovskii functional; absolute stability condition; delay decomposition technique; delay-decomposition approach; delay-dependent neural networks; delay-dependent stability criterion; globally asymptotic stability; time-varying delay; Asymptotic stability; Biological neural networks; Circuit stability; Delays; Stability criteria; Symmetric matrices; absolute stability; delay decomposition; neural networks; time varying delay;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location :
Qingdao
Print_ISBN :
978-1-4799-7016-2
Type :
conf
DOI :
10.1109/CCDC.2015.7162306
Filename :
7162306
Link To Document :
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