scholarly journals Exponential synchronization of discontinuous neural networks with time-varying mixed delays via state feedback and impulsive control

2014 ◽  
Vol 9 (2) ◽  
pp. 113-128 ◽  
Author(s):  
Xinsong Yang ◽  
Jinde Cao ◽  
Daniel W. C. Ho
Keyword(s):  
Neural Networks ◽  
State Feedback ◽  
Impulsive Control ◽  
Exponential Synchronization ◽  
Mixed Delays ◽  
Time Varying ◽  
Discontinuous Neural Networks

scholarly journals Almost Periodic Synchronization of Fuzzy Cellular Neural Networks with Time–Varying Delays via State–Feedback and Impulsive Control

2019 ◽  
Vol 29 (2) ◽  
pp. 337-349 ◽  
Author(s):  
Yongkun Li ◽  
Huimei Wang ◽  
Xiaofang Meng
Keyword(s):  
Neural Networks ◽  
State Feedback ◽  
Impulsive Control ◽  
Cellular Neural Networks ◽  
Banach Fixed Point Theorem ◽  
Time Varying ◽  
Fuzzy Cellular Neural Networks ◽  
Almost Periodic ◽  
Response Systems ◽  
Linear Differential

Abstract In this paper, we are concerned with drive-response synchronization for a class of fuzzy cellular neural networks with time varying delays. Based on the exponential dichotomy of linear differential equations, the Banach fixed point theorem and the differential inequality technique, we obtain the existence of almost periodic solutions of this class of networks. Then, we design a state feedback and an impulsive controller, and construct a suitable Lyapunov function to study the problem of global exponential almost periodic synchronization for the drive-response systems considered. At the end of the paper, we provide an example to verify the effectiveness of the theoretical results.

Global dissipativity and exponential synchronization of mixed time-varying delays neural networks with discontinuous activations

2020 ◽  
Vol 21 (7-8) ◽  
pp. 693-704 ◽  
Author(s):  
Kaifang Fei ◽  
Minghui Jiang ◽  
Meng Yan ◽  
Weizhen Liu
Keyword(s):  
Neural Networks ◽  
State Feedback ◽  
Exponential Synchronization ◽  
Matrix Measure ◽  
Analysis Techniques ◽  
Discontinuous Activations ◽  
Inclusion Theory ◽  
Generalized Halanay Inequality ◽  
New Criteria ◽  
Theoretical Results

AbstractIn this paper, the matters of dissipativity and synchronization for non-autonomous Hopfield neural networks with discontinuous activations are investigated. Firstly, under the framework of extending Filippov differential inclusion theory, several effective new criteria are derived. The global dissipativity of Filippov solution to neural networks is proved by using generalized Halanay inequality and matrix measure method. Secondly, the global exponential synchronization of the addressed network drive system and the response system is realized by utilizing inequality and some analysis techniques and designing the discontinuous state feedback controller. Finally, several numerical examples are given to verify the validity of the theoretical results.

SYNCHRONIZATION IN AN ARRAY OF HYBRID COUPLED NEURAL NETWORKS WITH MODE-DEPENDENT MIXED DELAYS AND MARKOVIAN SWITCHING

2009 ◽  
Vol 23 (09) ◽  
pp. 1171-1187 ◽  
Author(s):  
YANG TANG ◽  
RUNHE QIU ◽  
JIAN-AN FANG
Keyword(s):  
Neural Networks ◽  
Transition Probability ◽  
Distributed Delay ◽  
Mode Switching ◽  
Mixed Delays ◽  
Time Varying ◽  
Chaotic Neural Networks ◽  
Coupled Neural Networks ◽  
Hybrid Coupling ◽  
Coupling Time

In this letter, a general model of an array of N linearly coupled chaotic neural networks with hybrid coupling is proposed, which is composed of constant coupling, time-varying delay coupling and distributed delay coupling. The complex network jumps from one mode to another according to a Markovian chain with known transition probability. Both the coupling time-varying delays and the coupling distributed delays terms are mode-dependent. By the adaptive feedback technique, several sufficient criteria have been proposed to ensure the synchronization in an array of jump chaotic neural networks with mode-dependent hybrid coupling and mixed delays in mean square. Finally, numerical simulations illustrated by mode switching between two complex networks of different structure dependent on mode switching verify the effectiveness of the proposed results.

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