全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于信息耦合度的群集系统自组织分群方法

DOI: 10.13195/j.kzyjc.2013.1711, PP. 271-276

Keywords: 群集系统,自组织分群运动,信息熵,信息耦合度

Full-Text   Cite this paper   Add to My Lib

Abstract:

针对“速度平均”协同机制不能表征群集系统应激分群运动的问题,基于信息熵定义融合邻居速度、距离、数量及自身感知半径的信息耦合度指标,提出一种“min-max“形式的速度协同策略,结合“近距排斥-远距吸引”的位置协同,实现群集系统的自组织应激分群运动.数值仿真分析表明,基于该速度协同机制的群集能够完成一种概率意义上的等规模分群,且其组群效率优于传统基于速度平均机制的群集.

References

[1]  楚天广, 杨正东, 邓魁英, 等. 群体动力学与协调控制研究中的若干问题[J]. 控制理论与应用, 2010, 27(1): 86-93.
[2]  (Chu T G, Yang Z D, Deng K Y, et al. Problems in swarm dynamics and coordinated control[J]. Control Theory & Applications, 2010, 27(1): 86-93.)
[3]  Cavagna A, Cimarelli A, Giardina I, et al. Scale-free correlations in starling flocks[J]. Proc of the National Academy of Sciences of USA, 2010, 107(26): 11865.
[4]  Turgut A E, C? elikkanat H, G¨okc?e F, et al. Self-organized flocking in mobile robot swarms[J]. Swarm Intelligence, 2008, 2(2): 97-120.
[5]  雷小康, 刘明雍, 杨盼盼. 基于邻域跟随的群集系统分群控制算法[J]. 控制与决策, 2013, 28(5): 741-745.
[6]  (Lei X K, Liu M Y, Yang P P. Fission control algorithm for swarm based on local following interaction[J]. Control and Decision, 2013, 28(5): 741-745.)
[7]  刘明雍, 雷小康, 彭星光. 融合邻域自适应跟随的群集系统分群控制方法研究[J]. 西北工业大学学报, 2013, 31(2): 250-254.
[8]  (LiuMY, Lei X K, Peng X G. A control algorithm for flock fission based on adaptive local following interaction[J]. J of Northweatern Polytechnical University, 2013, 31(2): 250-254.)
[9]  Couzin I D, Laidre M E. Fission-fusion populations[J]. Current Biology, 2009, 19(15): 633-635.
[10]  Lee G, Chong N Y, Christensen H. Tracking multiple moving targets with swarms of mobile robots[J]. Intelligent Service Robotics, 2010, 3(2): 61-72.
[11]  Couzin I D, Krause J, Franks N R, et al. Effective leadership and decision-making in animal groups on the move[J]. Nature, 2005, 433(7025): 513-516.
[12]  Nabet B, Leonard N E, Couzin I D, et al. Dynamics of decision making in animal group motion[J]. J of Nonlinear Science, 2009, 19(4): 399-435.
[13]  Leonard N E, Shen T, Nabet B, et al. Decision versus compromise for animal groups in motion[J]. Proc of the National Academy of Sciences of USA, 2012, 109(1): 227-232.
[14]  Reynolds C W. Flocks, herds and schools: A distributed behavioral model[J]. ACM SIGGRAPH Computer Graphics, 1987, 21(4): 25-34.
[15]  Couzin I D, Krause J, James R, et al. Collective memory and spatial sorting in animal groups[J]. J of Theoretical Biology, 2002, 218(1): 1-11.
[16]  Olfati-Saber R. Flocking for multi-agent dynamic systems: Algorithms and theory[J]. IEEE Trans on Automatic Control, 2006, 51(3): 401-420.
[17]  Chen Z F, Liao H M, Chu T G. Clustering in multi-agent swarms via medium-range interaction[J]. Europhysics Letters, 2011, 96(4): 40015.
[18]  Wasserman E A, Young M E, Cook R G. Variability discrimination in humans and animals: Implications for adaptive action[J]. American Psychologist, 2004, 59(9): 879-890.
[19]  Bruce N D B, Tsotsos J K. Saliency, attention, and visual search: An information theoretic approach[J]. J of Vision, 2009, 9(3): 1-24.
[20]  Kullback S. Information theory and statistics[M]. New York: Dover Publications, 1997: 1-5.
[21]  Parrish J K, Edelstein-Keshet L. Complexity, pattern, and evolutionary trade-offs in animal aggregation[J]. Science, 1999, 284(5411): 99-101.
[22]  Couzin I D. Collective minds[J]. Nature, 2007, 445(7129): 715.
[23]  Bajec I L, Heppner F H. Organized flight in birds[J]. Animal Behaviour, 2009, 78(4): 777-789.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133