English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113303/144284 (79%)
Visitors : 50826499      Online Users : 692
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/73313


    Title: 以基因演算法優化最小二乘支持向量機於坐標轉換之研究
    Coordinate Transformation Using Genetic Algorithm Based Least Square Support Vector Machine
    Authors: 黃鈞義
    Contributors: 林老生
    Lin, Lao Sheng
    黃鈞義
    Keywords: 坐標轉換
    最小二乘法支持向量機
    六參數轉換
    基因演算法
    Coordinate Transformation
    Least Square Support Vector Machine
    6-parameter Transformation
    Genetic Algorithm
    Date: 2014
    Issue Date: 2015-02-03 10:29:12 (UTC+8)
    Abstract: 由於採用的地球原子不同,目前,台灣地區有兩種坐標系統存在,TWD67(Taiwan Datum 1967) 和TWD97(Taiwan Datum 1997)。在應用上,必須進行不同地球原子間之坐標轉換。坐標轉換方面,有許多方法可供選擇,如六參數轉換、支持向量機(Support Vector Machine, SVM)轉換等。
    最小二乘支持向量機(Least Square Support Vector Machine, LSSVM),為SVM的一種演算法,是一種非線性模型。LSSVM在運用上所需之參數少,能夠解決小樣本、非線性、高維度和局部極小點等問題。目前,LSSVM,已經被成功運用在影像分類和統計迴歸等領域上。
    本研究將利用LSSVM採用不同之核函數:線性核函數(LIN)、多項式核函數(POLY)及徑向基核函數(RBF)進行TWD97和TWD67之坐標轉換。研究中並使用基因演算法來調整LSSVM的RBF核函數之系統參數(後略稱RBF+GA),找出較佳之系統參數組合以進行坐標轉換。模擬與實測之地籍資料,將被用以測試LSSVM及六參數坐標轉換方法的轉換精度。
    研究結果顯示,RBF+GA在各實驗區之轉換精度優於參數優化前RBF之轉換精度,且RBF+GA之轉換精度也較六參數轉換之轉換精度高。
    進行參數優化後,RBF+GA相對於RBF的精度提升率如下:(1)模擬實驗區:參考點與檢核點數量比分別為1:1、2:1、3:1、1:2及1:3時,精度提升率分別為15.2%、21.9%、33.2%、12.0%、11.7%;(2)真實實驗區:花蓮縣、台中市及台北市實驗區之精度提升率分別為20.1%、32.4% 、22.5%。
    There are two coordinate systems with different geodetic datum in Taiwan region, i.e., TWD67 (Taiwan Datum 1967) and TWD97 (Taiwan Datum 1997). In order to maintain the consistency of cadastral coordinates, it is necessary to transform from one coordinate system to another. There are many coordinate transformation methods, such as, 2-dimension 6-parameter transformation, and support vector machine (SVM). Least Square Support Vector Machine (LSSVM), is one type of SVM algorithms, and it is also a non-linear model。LSSVM needs a few parameters to solve non-linear, high-dimension problems, and it has been successfully applied to the fields of image classification, and statistical regression. The goal of this paper is to apply LSSVM with different kernel functions (POLY、LIN、RBF) to cadastral coordinate transformation between TWD67 and TWD97.
    Genetic Algorithm will be used to find out an appropriate set of system parameters for LSSVM with RBF kernel to transform the cadastral coordinates. The simulated and real data sets will be used to test the performances, and coordinate transformation accuracies of LSSVM with different kernel functions and 6-parameter transformation.
    According to the test results, it is found that after optimizing the RBF parameters (RBF+GA), the transformation accuracies using RBF+GA are better than RBF, and even better than those of 6-parameter transformation.
    Comparing with the transformation accuracies using RBF, the transformation accuracy improving rate of RBF+GA are : (1) The simulated data sets: when the amount ratio of reference points and check points comes to 1:1, 2:1, 3:1, 1:2 and 1:3, the transformation accuracy improving rate are 15.2%, 21.9%, 33.2%, 12.0% and 11.7%, respectively; (2) The real data sets: the transformation accuracy improving rate of RBF+GA for the Hualien, Taichung and Taipei data sets are 20.1%, 32.4% and 22.5%, respectively.
    Reference: 一、中文部分
    王定成,2009,『支持向量機建模預測與控制』,北京,氣象出版社。
    王奕鈞,2005,「神經網路應用於地籍坐標轉換之研究」,國立政治大學地政系碩士論文:臺北。
    王繼剛,2009,「基於最小二乘支持向量機的區域GPS高程轉換組合」,『大地測量與地球動力學』,29(5):99-102。
    田玉剛,2007,「基於最小二乘支持向量機迴歸的GPS高程轉換參數」,『測繪工程第』,16(4):18-21。
    史峰、王小川、郁磊、李洋,2010,『MATLAB神經網路30個案例分析』,北京,北京航空航天大學出版社。
    白鵬、張喜斌等,2008,『支持向量機理論及工程應用實例』,西安,西安電子科技大學出版社。
    沈昱廷,2011,『以最小二乘支持向量機擬合區域性大地起伏值之研究-以台中為例』,國立中興大學土木工程學系碩士論文:臺中。
    何晨光、賀思德、董志民,2008,「最小二乘支持向量機在人臉識別中的應用」,『雲南大學學報』第30期:239-245。
    何維信,2004,『測量學』第六版,臺北,宏泰出版社。
    林老生、王奕鈞,2006,「應用神經網路在地籍資料TWD67與TWD97坐標轉換之研究」,『台灣土地研究』,10(1):53-69
    林怡君,2013,『利用最小一乘法在地籍坐標轉換資料偵錯之研究』,國立政治大學地政學系碩士論文:臺北。
    周伯韜、李安貴,2006,「最小二乘支持向量機的一種改進算法」,『南昌大學學報』,30(6):616-619
    周輝仁、任仙玲,2009,「最小二乘支持向量機的參數優選方法及應用」,『系統工程學報』,2:248-252
    范千,2008,「區域似大地水準面確定的最小二乘支持向量機方法」,『測繪工程』,17(5):001-003。
    姜華、曹紅妍,「基於最小二乘支持向量機的鐵路客運量預測研究」,『河南科學』,28(8):989-991。
    陳世平,2003,『數值法辦理圖解地籍圖數化區之土地複丈作業研究—以農地重測區為例』,逢甲大學土地管理學系碩士在職專班碩士論文:臺中。
    陳帥、朱建寧,2008,「區域似大地水準面確定的最小二乘支持向量機方法」,『華東理工大學學報』,34(2):278-282。
    張展羽、馮寶平,2005,「支持向量機在逕流預報中之應用探討」,『人民長江』36(8):038-039。
    張根寶、劉佳、王國強,2010,「基於遺傳算法和最小二乘支持向量機可靠性分配」,『計算機應用研究』,27(9):3300-3303。
    張智星,2007,『MATLAB程式設計入門篇』第一版,臺北,鈦思科技出版社。
    許皓寧,2003,『台北市地籍資料TWD67與TWD97坐標轉換之比較研究』,國立中興大學土木工程學系碩士論文:臺中。
    黃華尉,2001,『TWD97與TWD67二度坐標轉換之研究』,國立成功大學測量與空間資訊學系碩士論文:臺南。
    臺北市政府地政處測量大隊,2004,『台北市TWD67第及坐標系統轉換為TWD97坐標系統作業總報告』。
    趙洪波,2004,「基於遺傳算法的支持向量機研究」,『紹興文理學院學報』,24(9):025-028。
    二、外文參考文獻
    Avci, E, 2009, “Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm support vector machines: HGASVM”, Expert Systems with Applications, 36:1391–1402.
    Ghilani Charles, D. and Wolf, P. R., 2006, ADIUSTMENT COMPUTATIONS SPATIAL DATA ANALYSIS, 4th edition, New York: John Wiley& Sons, Inc.
    Farag, A. and Mohamed, R. M., (2004, December), Regression Using Support Vector Machines., on the World Wide Web: http://www.cvip.uofl.edu/wwwcvip/research/publications/TechReport/SVMRegressionTR.pdf.
    Fletcher, R., 1987, Practical methods of optimization, Chichester; New York: John Wiley& Sons, Inc.
    Gunn, S. R., (1998, May), Support Vector Machines for Classification and Regression, on the World Wide Web: http://users.ecs.soton.ac.uk/srg/publications/pdf.
    Pelckmans , K., Suykens, J. A. K., Gestel, T. V., Brabanter, J. D., Lukas, L., Hamers, B., Moor, B. D. and Vandewalle, J., LS-SVMlab: a MATLAB/C toolbox for Least Squares Support Vector Machines., on the World Wide Web: http://www.esat.kuleuven.be/sista/lssvmlab/old/lssvmlab_paper0.pdf.
    Shevade, K., Keerthi, S. S., Bhattacharyya, C. and Murthy, K. R. K., 2000, “Improvements to the SMO Algorithm for SVM Regression”, IEEE Transactionon Neural Networks, 11: 1188-1193.
    Smola, A, J. and Scholkopf, B., (1998, October), A Tutorial on Support Vector Regression, on the World Wide Web: http://www.svms.org/regression/SmSc98.pdf.
    Steinke, F., Schölkopf, B. and Blanz, V., 2005, “Support Vector Machines for 3D Shape Processing”, EUROGRAPHICS, 24(3): 285-294.
    Suykens, J. A. K., (2011, August), LS-SVMlab Toolbox User’s Guideversion 1.8, on the World Wide Web: http://www.esat.kuleuven.be/sista/lssvmlab.
    Suykens, J. A. K. and Vandewalle, J., 1999, “Least Squares Support Vector Machine Classifiers”, Neural Processing Letters, 9(3): 293–300.
    Vapnik, V., 1999, The Nature of Statistical Learning Theory, New York: Springer-Verlag, New York, Inc.
    Ye, J. and Xiong, T., (2007), SVM versus Least Squares SVMS, on the World Wide Web: http://jmlr.org/proceedings/papers/v2/ye07a/ye07a.pdf.
    Zuriani, M. and Yuhanis, Y, 2011, “Optimizing LSSVM Using ABC For Non-Volatile Financial Prediction”, Australian Journal of Basic and Applied Sciences, 9(11): 549–556

    三、網頁參考
    中央研院院計算中心,GIS應用支援工具集,取用日期 2014年7月,
    http://www.ascc.sinica.edu.tw/gis/ISTIS/tools.html
    LSSVMlab1.8,Math Works,取用日期 2014年7月,http://www.esat.kuleuven.be/sista/lssvmlab
    Description: 碩士
    國立政治大學
    地政研究所
    101257033
    103
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G1012570331
    Data Type: thesis
    Appears in Collections:[地政學系] 學位論文

    Files in This Item:

    File SizeFormat
    033101.pdf2968KbAdobe PDF2522View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback