Finite-Time Stabilization for Stochastic Inertial Neural Networks with Time-Delay via Nonlinear Delay Controller

Finite-Time Stabilization for Stochastic Inertial Neural Networks with Time-Delay via Nonlinear Delay Controller

This paper pays close attention to the problem of finite-time stabilization related to stochastic inertial neural networks with or without time-delay. By establishing proper Lyapunov-Krasovskii functional and making use of matrix inequalities, some sufficient conditions on finite-time stabilization...

Full description

Bibliographic Details
Main Authors: Deyi Li, Yuanyuan Wang, Guici Chen, Shasha Zhu
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2018/2939425
id doaj-9c5611cdc9514909a6fcc7b7c68b85f4
record_format Article
spelling doaj-9c5611cdc9514909a6fcc7b7c68b85f42020-11-25T00:47:01ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/29394252939425Finite-Time Stabilization for Stochastic Inertial Neural Networks with Time-Delay via Nonlinear Delay ControllerDeyi Li0Yuanyuan Wang1Guici Chen2Shasha Zhu3College of Science, Wuhan University of Science and Technology, Wuhan 430065, ChinaCollege of Science, Wuhan University of Science and Technology, Wuhan 430065, ChinaCollege of Science, Wuhan University of Science and Technology, Wuhan 430065, ChinaCollege of Science, Wuhan University of Science and Technology, Wuhan 430065, ChinaThis paper pays close attention to the problem of finite-time stabilization related to stochastic inertial neural networks with or without time-delay. By establishing proper Lyapunov-Krasovskii functional and making use of matrix inequalities, some sufficient conditions on finite-time stabilization are obtained and the stochastic settling-time function is also estimated. Furthermore, in order to achieve the finite-time stabilization, both delayed and nondelayed nonlinear feedback controllers are designed, respectively, in terms of solutions to a set of linear matrix inequalities (LMIs). Finally, a numerical example is provided to demonstrate the correction of the theoretical results and the effectiveness of the proposed control design method.http://dx.doi.org/10.1155/2018/2939425
collection DOAJ
language English
format Article
sources DOAJ
author Deyi Li
Yuanyuan Wang
Guici Chen
Shasha Zhu
spellingShingle Deyi Li
Yuanyuan Wang
Guici Chen
Shasha Zhu
Finite-Time Stabilization for Stochastic Inertial Neural Networks with Time-Delay via Nonlinear Delay Controller
Mathematical Problems in Engineering
author_facet Deyi Li
Yuanyuan Wang
Guici Chen
Shasha Zhu
author_sort Deyi Li
title Finite-Time Stabilization for Stochastic Inertial Neural Networks with Time-Delay via Nonlinear Delay Controller
title_short Finite-Time Stabilization for Stochastic Inertial Neural Networks with Time-Delay via Nonlinear Delay Controller
title_full Finite-Time Stabilization for Stochastic Inertial Neural Networks with Time-Delay via Nonlinear Delay Controller
title_fullStr Finite-Time Stabilization for Stochastic Inertial Neural Networks with Time-Delay via Nonlinear Delay Controller
title_full_unstemmed Finite-Time Stabilization for Stochastic Inertial Neural Networks with Time-Delay via Nonlinear Delay Controller
title_sort finite-time stabilization for stochastic inertial neural networks with time-delay via nonlinear delay controller
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2018-01-01
description This paper pays close attention to the problem of finite-time stabilization related to stochastic inertial neural networks with or without time-delay. By establishing proper Lyapunov-Krasovskii functional and making use of matrix inequalities, some sufficient conditions on finite-time stabilization are obtained and the stochastic settling-time function is also estimated. Furthermore, in order to achieve the finite-time stabilization, both delayed and nondelayed nonlinear feedback controllers are designed, respectively, in terms of solutions to a set of linear matrix inequalities (LMIs). Finally, a numerical example is provided to demonstrate the correction of the theoretical results and the effectiveness of the proposed control design method.
url http://dx.doi.org/10.1155/2018/2939425
work_keys_str_mv AT deyili finitetimestabilizationforstochasticinertialneuralnetworkswithtimedelayvianonlineardelaycontroller
AT yuanyuanwang finitetimestabilizationforstochasticinertialneuralnetworkswithtimedelayvianonlineardelaycontroller
AT guicichen finitetimestabilizationforstochasticinertialneuralnetworkswithtimedelayvianonlineardelaycontroller
AT shashazhu finitetimestabilizationforstochasticinertialneuralnetworkswithtimedelayvianonlineardelaycontroller
_version_ 1725262405693341696

Similar Items