English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 109077/140081 (78%)
Visitors : 43354187      Online Users : 730
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/140.119/68596

    Title: 應用粒子群演算法改善傳統二次曲面擬合區域性大地起伏-以台中地區為例
    Other Titles: Applying Particle Swarm Optimization to Improve Traditional Quadratic Surface Fitting Local Geoidal Undulation Model-A Case Study in Taichung Area
    Authors: 甯方璽;陳佳菱
    Ning, Fang-Shii;Chen, Chia-Ling
    Contributors: 地政系
    Keywords: 粒子群演算法;區域性大地起伏
    Particle Swarm Optimization;Local Geoid
    Date: 2014.05
    Issue Date: 2014-08-12 14:49:14 (UTC+8)
    Abstract: 本研究結合實驗區現有已施測一等水準點正高及水準點上GPS測得之平面坐標和橢球高,以粒子群演算法改善傳統二次曲面擬合區域性大地起伏精度,研究結果顯示,採用粒子群演算法改善傳統二次曲面所建立之區域性大地起伏模型,其檢核點的均方根誤差精度可達到± 1.02 cm,且不須計算任何參數。
    This study intends to integrate the existing 1st order leveling data, GPS coordinates and ellipsoidal height to find out the geoidal undulation model of Taichung city. The main simulation is done by adopting Particle Swarm Optimization to better fit local geoid by traditional quadratic surface. In accordance with the experiment, we propose an improved result by using the Particle Swarm Optimization and showing a computed Root Mean Square Error about ± 1.02cm without imposing any parametrical restrictions.
    Relation: 台灣土地研究, 17(1), 23-35
    Data Type: article
    Appears in Collections:[地政學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    2335.pdf2491KbAdobe PDF2872View/Open

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

    社群 sharing

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback