Output synchronization and H∞ output synchronization of complex dynamical networks with multiple derivative couplings-Reference-Cited by-同舟云学术

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Output synchronization and H∞ output synchronization of complex dynamical networks with multiple derivative couplings

Published:2019-01 Issue:1 Volume:356 Page:407-440
ISSN:0016-0032
Container-title:Journal of the Franklin Institute
language:en
Short-container-title:Journal of the Franklin Institute

Author:

Wang Dong-Yang,Wang Jin-Liang,Ren Shun-Yan,Huang Yan-Li

Funder

National Natural Science Foundation of China

Program for Innovative Research Team

Publisher

Elsevier BV

Subject

Applied Mathematics,Computer Networks and Communications,Signal Processing,Control and Systems Engineering

Reference38 articles.

1. Global synchronization of complex dynamical networks through digital communication with limited data rate;Wang;IEEE Trans. Neural Netw. Learn. Syst.,2015

2. Synchronization in directed complex networks using graph comparison tools;Liu;IEEE Trans. Circuits Syst. I Regul. Pap.,2015

3. Fuzzy complex dynamical networks and its synchronization;Mahdavi;IEEE Trans. Cybern.,2013

4. Synchronization probability in large complex networks;Manaffam;IEEE Trans. Circuits Syst. II Express Br.,2013

5. Exponential synchronization of complex networks of linear systems and nonlinear oscillators: a unified analysis;Qin;IEEE Trans. Neural Netw. Learn. Syst.,2015

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1. Adaptive Output Synchronization of Coupled Fractional-Order Memristive Reaction-Diffusion Neural Networks;Fractal and Fractional;2024-01-25

2. Finite-Time Nonchattering Synchronization of Coupled Neural Networks With Multi-Weights;IEEE Transactions on Network Science and Engineering;2023-07-01

3. Adaptive fixed-time output synchronization for complex dynamical networks with multi-weights;Neural Networks;2023-06

4. Output Synchronization of Coupled Fractional-Order Neural Networks With Multiple Weights;2022 41st Chinese Control Conference (CCC);2022-07-25

5. Adaptive quantitative control for robust H∞ synchronization between multiplex neural networks under stochastic cyber attacks;Neurocomputing;2022-07

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