Title :
Exponential Stability of a Class of Stochastic Interval Cellular Neural Networks
Author :
Han, Jin-fang ; Qiu, Ji-qing
Author_Institution :
Inst. of Eng. Math., Hebei Univ. of Sci. & Technol., Shijiazhuang, China
Abstract :
The exponential stability of a class of stochastic interval cellular neural networks with delay is investigated in this paper. For such neural networks, a kind of equivalent description is given ,and several sufficient conditions for the exponential stability in the mean square and surely exponential stability are established by the Lyapunov function method and lto formula. The criteria given here are generalizations of some provided in the earlier references.
Keywords :
Lyapunov methods; asymptotic stability; cellular neural nets; delays; neurocontrollers; Lyapunov function; cellular neural networks; delays; exponential stability; lto formula; Artificial intelligence; Cellular neural networks; Computational intelligence; Indium tin oxide; Lyapunov method; Neural networks; Robust stability; Stability criteria; Stochastic processes; Sufficient conditions; Delay; Exponential Stability; Lyapunov function; Stochastic interval Cellular Neural Networks; formula;
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
DOI :
10.1109/AICI.2009.483
