Title :
Exponential stability for stochastic neural networks of neutral-type with discrete and distributed time-delays
Author :
Zhu Qingyu ; Zhou Wuneng ; Mou Xiaozheng
Author_Institution :
Coll. of Inf. Sci. & Technol., Donghua Univ., Shanghai, China
Abstract :
In this paper, the global robust exponential stability is investigated for uncertain neural networks of neutral-type with mixed time delays. Based on Lyapunov-Krasovskii stability theory and stochastic analysis approaches, several new criteria and derived to guarantee the exponential stability of the system. A numerical example is given to demonstrate the applicability of our proposed stability criteria.
Keywords :
Lyapunov methods; asymptotic stability; delay systems; discrete time systems; neurocontrollers; stochastic systems; uncertain systems; Lyapunov-Krasovskii stability theory; discrete time-delay; distributed time-delay; global robust exponential stability; mixed time delays; neutral-type neural network; stochastic neural network; uncertain neural network; Artificial neural networks; Delay; Numerical stability; Stability criteria; Stochastic processes; Symmetric matrices; Distributed Time-delays; Exponential Stability; Linear Matrix Inequality (LMI); Neutral Neural Networks;
Conference_Titel :
Control Conference (CCC), 2010 29th Chinese
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-6263-6
