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 ...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.展开更多
文摘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.
