利用粒子群優化演算法改善分群演算法在訊號分群上之應用

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姓名

黃昆輝(Huang Kun) 查詢紙本館藏

畢業系所

通訊工程學系在職專班

論文名稱

利用粒子群優化演算法改善分群演算法在訊號分群上之應用
(To use Particle Swarm Optimization improve K-means Perform Mathematical Calculations in Clustering of Signal Group)

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摘要(中)

近年在無線通訊產業的蓬勃發展下，各無線系統也因為技術的突破與量產的實現下，現在無線產品已經廣泛的涵蓋在各種的生活應用上，如現有的手機、電腦、以及新世代研發的數位家電等等，硬體建設上如台北市地方政府建構的WIFLY，跟台北捷運文湖線，以及各學校教育單位在校區內的WIFI，無線系統的應用現在已經大量充斥在生活週遭的各個角落。但是相對的RF頻帶的使用上，在涵蓋的區域及訊號的強弱適當與否，或者甚至於該區域上的監測，皆是各系統在架設起來後需要去維護與管控的。因為各種系統的訊號涵蓋在整個環境的周圍，要如何去評估區分訊號便是本論文深入研究的部分與目的。
粒子群優化演算法（Particle Swarm Optimization），簡稱為PSO，此演算法效果具有收斂且運算快速的特性，是解決與應用在進世代類神經網路的研究與最佳化問題上的好方法。PSO在群體內的各粒子間，有著交互訊息溝通的特點，在相關性上可以提供相當適當的資訊，並且具有參數設定少、搜尋速度快、執行實現度高的優點。本論文主題則是以此，強化分群演算法（K-Means）的各粒子在群體間的相關性，提供足夠的權重以適當進行判斷、修正訊號在統計群體內正確的位置，並結合圖資系統呈現視覺化效果來呈現。

摘要(英)

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 .

關鍵字(中)

★ 分群演算法
★ 利用粒子群優化演算法
★ 模糊決策
★ 群體智能

關鍵字(英)

★ Swarm Intelligence
★ Fuzzy Decision
★ PSO
★ K-Means

論文目次

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

參考文獻

[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

指導教授

賀嘉律(Chia-Lo Ho)

審核日期

2010-7-22

推文