Exponential input-to-state stability of recurrent neural networks with multiple time-varying delays.
In this paper, input-to-state stability problems for a class of recurrent neural networks model with multiple time-varying delays are concerned with. By utilizing the Lyapunov-Krasovskii functional method and linear matrix inequalities techniques, some sufficient conditions ensuring the exponential input-to-state stability of delayed network systems are firstly obtained. Two numerical examples and its simulations are given to illustrate the efficiency of the derived results.
Department of Mathematics, Key Laboratory for Optimization and Control of Ministry of Education, Chongqing Normal University, Chongqing, 400047 China.
Zhichun
Z
Yang
Department of Mathematics, Chongqing Normal University, Chongqing, 400047 China.
Weisong
W
Zhou
Texas A&M University at Qatar, PO Box 23874, Doha, Qatar.
Tingwen
T
Huang
Exponential input-to-state stability (exp-ISS); Multiple time-varying delays; Recurrent neural networks