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    請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/44534


    題名: 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
    作者: 黃昆輝;Huang Kun
    貢獻者: 通訊工程研究所碩士在職專班
    關鍵詞: 分群演算法;利用粒子群優化演算法;模糊決策;群體智能;Swarm Intelligence;Fuzzy Decision;PSO;K-Means
    日期: 2010-07-22
    上傳時間: 2010-12-09 13:48:01 (UTC+8)
    出版者: 國立中央大學
    摘要: 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。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 .
    顯示於類別:[通訊工程學系碩士在職專班 ] 博碩士論文

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