Impulsive Synchronization of Derivative Coupled Neural Networks With Cluster-Tree Topology

2020 ◽  
Vol 7 (3) ◽  
pp. 1788-1798
Author(s):  
Ze Tang ◽  
Ju H. Park ◽  
Yan Wang ◽  
Jianwen Feng
Keyword(s):  
Neural Networks ◽  
Tree Topology ◽  
Cluster Tree ◽  
Coupled Neural Networks ◽  
Impulsive Synchronization ◽  
Cluster Tree Topology

Impulsive Synchronization of Derivative CNNs with Cluster-Tree Topology

2021 ◽  
pp. 37-59
Author(s):  
Ze Tang ◽  
Dong Ding ◽  
Yan Wang ◽  
Zhicheng Ji ◽  
Ju H. Park
Keyword(s):  
Tree Topology ◽  
Cluster Tree ◽  
Impulsive Synchronization ◽  
Cluster Tree Topology

scholarly journals Improved Cluster-tree Topology Adapted for Indoor environement in Zigbee Sensor Network

2016 ◽  
Vol 94 ◽  
pp. 272-279 ◽  
Author(s):  
Mourad Ouadou ◽  
Ouadoudi Zytoune ◽  
Driss Aboutajdine ◽  
Yassin El Hillali ◽  
Atika Menhaj-Rivenq
Keyword(s):  
Sensor Network ◽  
Tree Topology ◽  
Cluster Tree ◽  
Cluster Tree Topology

scholarly journals Energy Efficient Hardware and Improved Cluster-Tree Topology for Lifetime Prolongation in ZigBee Sensor Networks

2017 ◽  
Vol 6 (4) ◽  
pp. 22 ◽  
Author(s):  
Mourad Ouadou ◽  
Ouadoudi Zytoune ◽  
Yassin El Hillali ◽  
Atika Menhaj-Rivenq ◽  
Driss Aboutajdine
Keyword(s):  
Sensor Networks ◽  
Energy Efficient ◽  
Tree Topology ◽  
Cluster Tree ◽  
Cluster Tree Topology

scholarly journals Synchronization of Time Delay Coupled Neural Networks Based on Impulsive Control

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Jie Fang ◽  
Yin Zhang ◽  
Danying Xu ◽  
Junwei Sun
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Control Method ◽  
Impulsive Control ◽  
Lyapunov Stability Theory ◽  
Control Effect ◽  
Coupled Neural Networks ◽  
Impulsive Synchronization ◽  
The Stability ◽  
Delayed Impulses

This paper investigates the impulsive synchronization of time delay coupled neural networks. Based on the Lyapunov stability theory and impulsive control method, a distributed delayed impulsive controller is designed to realize synchronization of the coupled neural networks. A new impulsive delayed inequality is proposed, where the control effect of distributed delayed impulses is fully considered. In addition, a suitable Lyapunov-like function is established to prove the stability of the synchronization system. Numerical simulation examples are introduced to illustrate the effectiveness and feasibility of the main results.

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