Mean Square Consensus under Coopetitive Social Networks with Communication Noise

被引：0

作者：

Cai, Hanzhe ^{[1
]}

Yuan, Fanli ^{[1
]}

Liang, Haili ^{[1
]}

Zhou, Zhao ^{[2
]}

机构：

[1] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai Key Lab Power Stn Automat Technol, Shanghai 200444, Peoples R China

[2] East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

来源：

2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC) | 2021年

基金：

中国国家自然科学基金; 国家重点研发计划;

关键词：

Opinion dynamic; Coopetitive networks; Undirected graph; Altafini model; Communication noise; MULTIAGENT SYSTEMS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper aims to investigate a stochastic Altafini model, in which individuals are disturbed by the communication noise. We use coopetitive networks to describe their affiliation of classes. Different from the general situations with noises, when the opinion values in the classes converge, the noise will decrease. We consider the communication noise, when the differences of opinion values are larger, the influences of noise are greater. When the opinion values are reaching to biparite consensus, the influences of noise are decreasing, such a design is more in line with the reality. By constructing the appropriate Lyapunov function, the conditions for the stochastic stability of the system are obtained. We get the conclusion of mean square biparite consensus. In addition, numerical simulation results can verify the theoretical results.

引用

页码：800 / 805

页数：6

共 50 条

[41] Consensus-Based Distributed Mean Square State Estimation
de Souza, Carlos E.
Kinnaert, Michel
Coutinho, Daniel
[J]. 2015 AMERICAN CONTROL CONFERENCE (ACC), 2015, : 5134 - 5139
[42] Mean Square Consensus for Uncertain Multiagent Systems with Noises and Delays
Sun, Yuangong
[J]. ABSTRACT AND APPLIED ANALYSIS, 2012,
[43] Distributed average consensus with least-mean-square deviation
Xiao, Lin
Boyd, Stephen
Kim, Seung-Jean
[J]. JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 2007, 67 (01) : 33 - 46
[44] MEASURING MEAN-SQUARE VALUE OF NOISE SIGNALS
POVKH, IL
EREMIN, GP
KONDRATE.VG
PLYUSNIN, NI
YATSENKO, AI
[J]. PRIBORY I TEKHNIKA EKSPERIMENTA, 1974, (02): : 122 - 125
[45] An Incremental Noise Constrained Least Mean Square Algorithm
Hameed, Usman
Khawaja, Sajid Gul
Bin Saeed, Muhammad Omer
[J]. 2019 5TH INTERNATIONAL CONFERENCE ON FRONTIERS OF SIGNAL PROCESSING (ICFSP 2019), 2019, : 26 - 30
[46] Mean-Square Consensus Seeking over Multi-Interconnection Dynamical Networks with Markovian Switching Topologies
Ao, Dun
Guo, Xu-dong
[J]. INTERNATIONAL CONFERENCE ON COMPUTER, MECHATRONICS AND ELECTRONIC ENGINEERING (CMEE 2016), 2016,
[47] Mean square state estimation for sensor networks
de Souza, Carlos E.
Coutinho, Daniel
Kinnaert, Michel
[J]. AUTOMATICA, 2016, 72 : 108 - 114
[48] Distributed Calibration for Sensor Networks under Communication Errors and Measurement Noise
Stankovic, Milos S.
Stankovic, Srdjan S.
Johansson, Karl Henrik
[J]. 2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2012, : 1380 - 1385
[49] Minimum mean square estimation and neural networks
Manry, MT
Apollo, SJ
Yu, Q
[J]. NEUROCOMPUTING, 1996, 13 (01) : 59 - 74
[50] The mean square exponential synchronization for stochastic complex networks perturbed with Poisson noise via a single controller
Zhang, Le
Fong, Jianwen
Zhao, Yi
[J]. PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019), 2019, : 1252 - 1257

← 1 2 3 4 5 →