Title :
Almost sure exponential stability of stochastic recurrent neural networks with time-varying delays
Author :
Wan, Li ; Zhou, Qinghua
Author_Institution :
Coll. of Sci., Wuhan Textile Univ., Wuhan, China
Abstract :
The stability of stochastic recurrent neural networks with time-varying delays is investigated. When the delay functions are not differentiable or the information on their derivation is unknown, the sufficient criteria on almost sure exponential stability have been established. One example is also given to illustrate the effectiveness of our results.
Keywords :
asymptotic stability; delays; recurrent neural nets; stability criteria; stochastic processes; delay function; exponential stability; stochastic recurrent neural network; time-varying delay; Artificial neural networks; Asymptotic stability; Delay; Recurrent neural networks; Stability criteria; Stochastic processes;
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
DOI :
10.1109/ICNC.2010.5582991
