Finite-time stabilization of uncertain delayed-hopfield neural networks with a time-varying leakage delay via non-chattering control  

作　　者：AOUITI Chaouki LI XiaoDi MIAADI Foued 

机构地区：[1]University of Carthage, Faculty of Sciences ofBizerta, Department of Mathematics, Research Units of Mathematics and Applications UR13ES47, Bizerta 7021, Tunisia [2]School of Mathematics and Statistics, Shandong Normal University, Ji'nan 250014, China

出　　处：《Science China(Technological Sciences)》2019年第7期1111-1122,共12页中国科学（技术科学英文版）

摘　　要：This article is concerned with the finite-time stabilization(FTSB) of a class of delayed-Hopfield neural networks with a timevarying delay in the leakage term in the presence of parameter uncertainties. To accomplish the target of FTSB, two new finitetime controllers are designed for uncertain delayed-Hopfield neural networks with a time-varying delay in the leakage term. By utilizing the finite-time stability theory and the Lyapunov-Krasovskii functional(LKF) approach, some sufficient conditions for the FTSB of these neural networks are established. These conditions, which can be used for the selection of control parameters,are in the form of linear matrix inequalities(LMIs) and can be numerically checked. Additionally, an upper bound of the settling time was estimated. Finally, our theoretical results are further substantiated by two numerical examples with graphical illustrations to demonstrate the effectiveness of the results.This article is concerned with the finite-time stabilization(FTSB) of a class of delayed-Hopfield neural networks with a timevarying delay in the leakage term in the presence of parameter uncertainties. To accomplish the target of FTSB, two new finitetime controllers are designed for uncertain delayed-Hopfield neural networks with a time-varying delay in the leakage term. By utilizing the finite-time stability theory and the Lyapunov-Krasovskii functional(LKF) approach, some sufficient conditions for the FTSB of these neural networks are established. These conditions, which can be used for the selection of control parameters,are in the form of linear matrix inequalities(LMIs) and can be numerically checked. Additionally, an upper bound of the settling time was estimated. Finally, our theoretical results are further substantiated by two numerical examples with graphical illustrations to demonstrate the effectiveness of the results.

关 键 词：neural networks FINITE-TIME stabilization parametric uncertainties LEAKAGE DELAY LYAPUNOV-KRASOVSKII functional LMI 

分 类 号：TH[机械工程]