Finite-time bipartite synchronization of switched competitive neural networks with time delay via quantized control

2021 ◽  
Author(s):  
Yi Zou ◽  
Housheng Su ◽  
Rongqiang Tang ◽  
Xinsong Yang
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Finite Time ◽  
Quantized Control

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

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Deyi Li ◽  
Yuanyuan Wang ◽  
Guici Chen ◽  
Shasha Zhu
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Finite Time ◽  
Design Method ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Nonlinear Feedback ◽  
Inertial Neural Networks ◽  
Finite Time Stabilization

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.

Finite-time boundedness for time-delay neural networks with external disturbances via weight learning approaches

2019 ◽  
Vol 33 (28) ◽  
pp. 1950343 ◽  
Author(s):  
Zhilian Yan ◽  
Youmei Zhou ◽  
Xia Huang ◽  
Jianping Zhou
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Finite Time ◽  
Weight Matrix ◽  
External Disturbances ◽  
Connection Weight ◽  
Learning Rules ◽  
Finite Time Boundedness ◽  
Weight Learning ◽  
Matrix Case

This paper addresses the issue of finite-time boundedness for time-delay neural networks with external disturbances via weight learning. With the help of a group of inequalities and combining with the Lyapunov theory, weight learning rules are devised to ensure the neural networks to be finite-time bounded for the fixed connection weight matrix case and the fixed delayed connection weight matrix case, respectively. Sufficient conditions on the existence of the desired learning rules are presented in the form of linear matrix inequalities, which are easily verified by MATLAB software. It is shown that the proposed learning rules also guarantee the finite-time stability of the time-delay neural networks. Finally, a numerical example is employed to show the applicability of the devised weight learning rules.

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