Title :
Global asymptotic stability analysis for stochastic neutral-type delayed neural networks
Author :
Wei Feng ; Haixia Wu
Author_Institution :
Dept. of Comput. Sci., Chongqing Educ. Coll., Chongqing, China
Abstract :
In this paper, the global asymptotic stability is investigated for a class of neutral stochastic neural networks with time-varying delays. Based on Lyapunov stability theory and stochastic analysis approaches, delay-dependent criterion is derived to ensure the global, asymptotic stability of the addressed system in the mean square. The criterion can be checked easily by the LMI Control Toolbox in Matlab. A numerical example is given to illustrate the feasibility and effectiveness of the results.
Keywords :
Lyapunov methods; asymptotic stability; delays; linear matrix inequalities; neural nets; stochastic processes; time-varying systems; LMI Control Toolbox; Lyapunov stability theory; delay-dependent criterion; delayed neural networks; global asymptotic stability; linear matrix inequalities; mean square method; stochastic analysis; stochastic neutral-type neural networks; time-varying delays; Asymptotic stability; Biological neural networks; Delay; Lyapunov method; Manipulator dynamics; Mathematical model; Neural networks; Neurotransmitters; Stability analysis; Stochastic processes; LMI; Stability; Stochastic Neutral-Type Delayed Neural Networks;
Conference_Titel :
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location :
Xuzhou
Print_ISBN :
978-1-4244-5181-4
Electronic_ISBN :
978-1-4244-5182-1
DOI :
10.1109/CCDC.2010.5498735
