Title page for 965303023


[Back to Results | New Search]

Student Number 965303023
Author Huang Kun(黃昆輝)
Author's Email Address No Public.
Statistics This thesis had been viewed 704 times. Download 10 times.
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

    [Back to Results | New Search]


    Browse | Search All Available ETDs

    If you have dissertation-related questions, please contact with the NCU library extension service section.
    Our service phone is (03)422-7151 Ext. 57407,E-mail is also welcomed.