DocumentCode
232006
Title
Dissipativity results for stochastic neural networks with mixed time-varying delays
Author
Zhao Zhenjiang ; Song Qiankun
Author_Institution
Dept. of Math., Huzhou Teachers Coll., Huzhou, China
fYear
2014
fDate
28-30 July 2014
Firstpage
5003
Lastpage
5007
Abstract
In this paper, the dissipativity is investigated for stochastic neural networks with discrete time-varying delay and distributed time-varying delay. By constructing appropriate Lyapunov-Krasovskii functionals and employing inequality technique, a criterion for checking the dissipativity of the addressed neural networks is established in terms of LMIs, which can be checked numerically using the effective LMI toolbox in MATLAB.
Keywords
Lyapunov methods; delays; linear matrix inequalities; neural nets; LMI; Lyapunov-Krasovskii functionals; Matlab; discrete time-varying delay; dissipativity results; distributed time-varying delay; inequality technique; linear matrix inequalities; mixed time-varying delays; stochastic neural networks; Delay effects; Delays; Neural networks; Numerical stability; Stability criteria; Stochastic processes; Discrete Time-Varying Delay; Dissipativity; Distributed Time-Varying Delay; Neural Networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference (CCC), 2014 33rd Chinese
Conference_Location
Nanjing
Type
conf
DOI
10.1109/ChiCC.2014.6895789
Filename
6895789
Link To Document