Suppr超能文献

一种考虑随机数据丢失的复杂动态网络新模型。

A New Model for Complex Dynamical Networks Considering Random Data Loss.

作者信息

Wu Xu, Jiang Guo-Ping, Wang Xinwei

机构信息

School of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Jiangsu Engineering Lab for IOT Intelligent Robots(IOTRobot), Nanjing 210023, China.

出版信息

Entropy (Basel). 2019 Aug 15;21(8):797. doi: 10.3390/e21080797.

Abstract

Model construction is a very fundamental and important issue in the field of complex dynamical networks. With the state-coupling complex dynamical network model proposed, many kinds of complex dynamical network models were introduced by considering various practical situations. In this paper, aiming at the data loss which may take place in the communication between any pair of directly connected nodes in a complex dynamical network, we propose a new discrete-time complex dynamical network model by constructing an auxiliary observer and choosing the observer states to compensate for the lost states in the coupling term. By employing Lyapunov stability theory and stochastic analysis, a sufficient condition is derived to guarantee the compensation values finally equal to the lost values, namely, the influence of data loss is finally eliminated in the proposed model. Moreover, we generalize the modeling method to output-coupling complex dynamical networks. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed model.

摘要

模型构建是复杂动态网络领域中一个非常基础且重要的问题。随着状态耦合复杂动态网络模型的提出,通过考虑各种实际情况引入了多种复杂动态网络模型。本文针对复杂动态网络中任意一对直接相连节点之间通信时可能发生的数据丢失问题,通过构建辅助观测器并选择观测器状态来补偿耦合项中丢失的状态,提出了一种新的离散时间复杂动态网络模型。利用李雅普诺夫稳定性理论和随机分析,推导出一个充分条件,以保证补偿值最终等于丢失值,即在提出的模型中最终消除数据丢失的影响。此外,我们将建模方法推广到输出耦合复杂动态网络。最后,给出两个数值例子来证明所提模型的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8efe/7515327/4952db2226a3/entropy-21-00797-g001.jpg

相似文献

1
A New Model for Complex Dynamical Networks Considering Random Data Loss.
Entropy (Basel). 2019 Aug 15;21(8):797. doi: 10.3390/e21080797.
2
Passivity and Output Synchronization of Complex Dynamical Networks With Fixed and Adaptive Coupling Strength.
IEEE Trans Neural Netw Learn Syst. 2018 Feb;29(2):364-376. doi: 10.1109/TNNLS.2016.2627083. Epub 2016 Nov 24.
3
Synchronization of complex dynamical networks with random coupling delay and actuator faults.
ISA Trans. 2019 Nov;94:57-69. doi: 10.1016/j.isatra.2019.03.029. Epub 2019 Apr 11.
4
An Event-Triggered Pinning Control Approach to Synchronization of Discrete-Time Stochastic Complex Dynamical Networks.
IEEE Trans Neural Netw Learn Syst. 2018 Dec;29(12):5812-5822. doi: 10.1109/TNNLS.2018.2812098. Epub 2018 Mar 29.
5
Stochastic quasi-synchronization of heterogeneous delayed impulsive dynamical networks via single impulsive control.
Neural Netw. 2021 Jul;139:223-236. doi: 10.1016/j.neunet.2021.03.011. Epub 2021 Mar 18.
6
A model of gene expression based on random dynamical systems reveals modularity properties of gene regulatory networks.
Math Biosci. 2016 Jun;276:82-100. doi: 10.1016/j.mbs.2016.03.008. Epub 2016 Mar 30.
8
Synchronization of complex dynamical networks via impulsive control.
Chaos. 2007 Dec;17(4):043126. doi: 10.1063/1.2803894.
9
Synchronization of Stochastic Complex Dynamical Networks Subject to Consecutive Packet Dropouts.
IEEE Trans Cybern. 2021 Jul;51(7):3779-3788. doi: 10.1109/TCYB.2019.2907279. Epub 2021 Jun 23.
10

引用本文的文献

1
Routing Strategies for Isochronal-Evolution Random Matching Network.
Entropy (Basel). 2023 Feb 16;25(2):363. doi: 10.3390/e25020363.

本文引用的文献

1
Synchronization in Fractional-Order Complex-Valued Delayed Neural Networks.
Entropy (Basel). 2018 Jan 12;20(1):54. doi: 10.3390/e20010054.
2
Role of time scales and topology on the dynamics of complex networks.
Chaos. 2019 Mar;29(3):033119. doi: 10.1063/1.5063753.
3
Synchronization Analysis of Two-Time-Scale Nonlinear Complex Networks With Time-Scale-Dependent Coupling.
IEEE Trans Cybern. 2019 Sep;49(9):3255-3267. doi: 10.1109/TCYB.2018.2839648. Epub 2018 Jun 13.
4
Nonlinear stratospheric variability: multifractal de-trended fluctuation analysis and singularity spectra.
Proc Math Phys Eng Sci. 2016 Jul;472(2191):20150864. doi: 10.1098/rspa.2015.0864.
6
Control of Nonlinear Networked Systems With Packet Dropouts: Interval Type-2 Fuzzy Model-Based Approach.
IEEE Trans Cybern. 2015 Nov;45(11):2378-89. doi: 10.1109/TCYB.2014.2371814. Epub 2014 Dec 2.
7
H∞ state estimation for complex networks with uncertain inner coupling and incomplete measurements.
IEEE Trans Neural Netw Learn Syst. 2013 Dec;24(12):2027-37. doi: 10.1109/TNNLS.2013.2271357.
8
Robustness to noise in synchronization of complex networks.
Sci Rep. 2013;3:2026. doi: 10.1038/srep02026.
9
Emergence of scaling in random networks.
Science. 1999 Oct 15;286(5439):509-12. doi: 10.1126/science.286.5439.509.
10
Collective dynamics of 'small-world' networks.
Nature. 1998 Jun 4;393(6684):440-2. doi: 10.1038/30918.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验