Group decision making with multiple leaders: local rules, weighted networks and consensus

Fei Chen; Zengqiang Chen; Zhongxin Liu; Linying Xiang; Zhuzhi Yuan

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Group decision making with multiple leaders: local rules, weighted networks and consensus

机译：与多位领导者进行集体决策：当地规则，加权网络和共识

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In this paper, a realistic and simple discrete-time model based on nearest neighbour rules for describing the movements of autonomous agents is introduced. Two kinds of networks are considered here:rn1. weighted directed networks with fixed topologyrn2. weighted directed networks with switching topology.rnThe analysis is based on a blend of graph-theoretic and system-theoretic tools. In addition to the stability analysis, the steady state of convergence is also analysed. It turns out that homogenous digraphs plays a central role in addressing average-consensus problems, where the notation 'leadership' is introduced to measure the influence of a node to the consensus of the group. Finally, numerical examples are included which find very good agreements with the analytical results.

机译：本文介绍了一种基于最近邻规则的现实且简单的离散时间模型，用于描述自治主体的运动。这里考虑两种网络：rn1。具有固定拓扑的加权定向网络具有交换拓扑的加权有向网络。分析基于图论和系统理论工具的混合。除了稳定性分析之外，还分析了收敛的稳态。事实证明，同质有向图在解决平均共识问题中发挥着核心作用，其中引入了“领导力”概念来衡量节点对群体共识的影响。最后，提供了一些数值示例，这些示例与分析结果非常吻合。

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来源
《International journal of systems,control and communications》 |2008年第2期|227-239|共13页
作者
Fei Chen; Zengqiang Chen; Zhongxin Liu; Linying Xiang; Zhuzhi Yuan;
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作者单位

Department of Automation, Nankai University, Tianjin, 300071, PR China;

Department of Automation, Nankai University, Tianjin, 300071, PR China;

Department of Automation, Nankai University, Tianjin, 300071, PR China;

Department of Automation, Nankai University, Tianjin, 300071, PR China;

Department of Automation, Nankai University, Tianjin, 300071, PR China;

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正文语种 eng
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关键词
consensus; multi-agent system; multiple leader; graph laplacian; local rule; complex system; switching topology; weighted graph;

机译：共识;多代理系统;多个领导者图拉普拉斯地方统治复杂的系统交换拓扑;加权图;

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Group decision making with multiple leaders: local rules, weighted networks and consensus

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