Title :
Almost sure stability of stochastic recurrent neural networks with unbounded distributed delays
Author_Institution :
Sch. of Manage., Univ. of Leicester, Leicester, UK
Abstract :
This paper deals with a class of stochastic recurrent neural networks with unbounded distributed delays. By using the LaSalle invariant principle of stochastic differential delay equations, the Ito´s formula and a linear matrix inequality approach, a new set of sufficient conditions to ensure the almost sure stability of the considered system. Moreover, an example is provided to illustrate the effectiveness of the obtained result.
Keywords :
delay-differential systems; linear matrix inequalities; recurrent neural nets; stability; stochastic processes; Ito´s formula; LaSalle invariant principle; almost sure stability; linear matrix inequality approach; stochastic differential delay equation; stochastic recurrent neural network; unbounded distributed delay; Artificial neural networks; Biological neural networks; Delay; Recurrent neural networks; Stability criteria; Stochastic processes; LaSalle invariant principle; almost sure stability; linear matrix inequality; stochastic recurrent neural networks; unbounded distributed delays;
Conference_Titel :
Consumer Electronics, Communications and Networks (CECNet), 2011 International Conference on
Conference_Location :
XianNing
Print_ISBN :
978-1-61284-458-9
DOI :
10.1109/CECNET.2011.5768827
