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Student Number 965303023
Author Huang Kun(黃昆輝)
Author's Email Address No Public.
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Department Executive Master of Communication Engineering
Year 2009
Semester 2
Degree Master
Type of Document Master's Thesis
Language zh-TW.Big5 Chinese
Title To use Particle Swarm Optimization improve K-means Perform Mathematical Calculations in Clustering of Signal Group
Date of Defense 2010-07-17
Page Count 30
Keyword
  • Fuzzy Decision
  • K-Means
  • PSO
  • Swarm Intelligence
  • Abstract Because the flourishing development of the wireless communication industry during these years , realization that every wireless system has because of break-through and quantity of technology too , the wireless products have been already extensive use in various life , existing mobile phone, computer, and new to research and develop generation digit of the Electrical home appliances , etc. Build WIFLY constructed of the Taipei local government on the construction of hardware , and Taipei MRT of Wenhu(文湖) Line , and WIFI in many school education , the wireless system application use in a large amount now . But in RF frequency band use , signal power of area contained appropriate or not , or the monitoring on this area , there were every system that needed to maintain and in charge of accusing after erecting . Because various system of signals contain whole around the environment , this thesis further investigates how to go to assess the signal of classify .
    Particle Swarm Optimization (PSO) , it has convergence and fast operation of characteristic that this performs algorithms , this is a good method on the optimization problem generation that solve and apply to the research of neural networks . Every particle in the colony of PSO , There is mutual information communication characteristic , it can offer appropriate information on relevance , and also parameters are set up less 、 fast to search 、 easy to realized . This thesis theme use this to improve K-Means particles relevance among the colonies , offer enough weights by appropriately judging 、 revision of correct signal position in the colony , and combine map system to appear vision result .
    Table of Content 摘 要................................................................................................................................................................ 一
    ABSTRACT .......................................................................................................................................................... 2
    誌 謝.................................................................................................................................................................. 4
    目 錄...................................................................................................................................................................... I
    圖 目 錄............................................................................................................................................................... II
    第一章 緒 論................................................................................................................................................... 1
    1.1 研究動機............................................................................................................................................. 1
    1.2 研究目的............................................................................................................................................. 2
    1.3 論文架構............................................................................................................................................. 3
    第二章 利用粒子群優化演算法提供權重影響分群演算法分群決策.......................................................... 6
    2.1 分群演算法介紹................................................................................................................................. 6
    2.2 粒子群優化演算法介紹...................................................................................................................... 8
    2.3 粒子群優化演算法改善分群演算法介紹........................................................................................ 11
    第三章 利用C語言模擬演算法的實行步驟................................................................................................ 17
    第四章 比較使用粒子群優化演算法改善後的結果.................................................................................... 20
    第五章 延伸應用........................................................................................................................................... 26
    5.1 多階層式訊號搜尋............................................................................................................................ 26
    5.2 調適性智能學習參數........................................................................................................................ 27
    5.3 記憶型人工智慧學習法.................................................................................................................... 28
    參考文獻.............................................................................................................................................................. 29
    Reference [1]Eberhart, R.C. and J. Kennedy, “A new optimizer using particle swarm theory. in Proceedings of the Sixth International Symposium on Micro Machine and Human Science.”, Nagoya, Japan.1995.
    [2]Vance Faber, “Clustering and the Continuous k-Means Algorithm” , Los Alamos Science, Number 22 1994
    [3]Jason Tillett1, T.M. Rao2, Ferat Sahin3 and Raghuveer Rao3 , “Darwinian Particle Swarm Optimization” , University of Rochester Rochester , NY USA
    [4]Xiaohui Cui, Thomas E. Potok , “Document Clustering Analysis Based on Hybrid PSO+K-means Algorithm”, Applied Software Engineering Research Group, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6085, USA
    [5]Gereon Frahling , Christian Sohler , “A fast k-means implementation using coresets “, Heinz Nixdorf Institute and Department of Computer Science University of Paderborn D-33102 Paderborn, Germany , December 5, 2005
    [6]J. Macqueen , “Some methods for classification and analysis of multivariate observations” , University of California , Los Angeles
    [7]Andrew W. Moore , “K-means and Hierarchical Clustering” , Professor School of Computer Science Carnegie Mellon University
    [8]Timothy Verbeemen , Gert Storms , Tom Verguts , “Varying Abstraction in Categorization: a K-means Approach” , Departement Psychologie, University of Leuven
    [9]Marco A. Montes de Oca , “Particle Swarm Optimization Introduction” , IRIDIA-CoDE, Universit´e Libre de Bruxelles (U.L.B.) May 7, 2007
    [10]Xiaohui Cui, Thomas E. Potok , Paul Palathingal , “Document Clustering using Particle Swarm Optimization” , Applied Software Engineering Research Group Computational Sciences and Engineering Division
    [11]Kiri L. Wagstaff , Benjamin Bornstein , “K-means in Space: A Radiation Sensitivity Evaluation” , Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109 USA
    [12]Ching-Han Chen , “Particle Swarm Optimization” , I-Shou University 2006-06-06
    [13]楊正宏 , 蕭智仁 , 莊麗月 , “K-means 結合混沌PSO 應用於資料分群問題” , 第二十屆國際資訊管理學術研討會 , ICIM2009
    [14]李維平 , 黃郁授 , 戴彰廷 , “自適應慣性權重改良粒子群演算法之研究” , 中原大學資訊管理所
    [15]陳同孝, 陳雨霖 , 劉明山 , 許文綬 , 林志強 , 邱永興 , “結合K-means及階層式分群法之二階段分群演算法” , 國立臺中技術學院 資訊科技與應用研究所
    [16]陳振雄 , 謝政勳 , “雷達回波訊號來向角之定位” , 建國科大學報:工程類 2005,25(1),113-124
    Advisor
  • Chia-Lo Ho(賀嘉律)
  • Files
  • 965303023.pdf
  • disapprove authorization
    Date of Submission 2010-07-22

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