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
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;
Conference_Titel :
Control Conference (CCC), 2014 33rd Chinese
Conference_Location :
Nanjing
DOI :
10.1109/ChiCC.2014.6895789
