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基因表達程式規劃於全球定位系統中幾何精度稀釋因子之逼近 = GPS GD...
~
何亞威
基因表達程式規劃於全球定位系統中幾何精度稀釋因子之逼近 = GPS GDOP Approximation Using Genetic Expression Programming
Record Type:
Language materials, printed : monographic
Paralel Title:
GPS GDOP Approximation Using Genetic Expression Programming
Author:
何亞威,
Secondary Intellectual Responsibility:
國立高雄大學
Place of Publication:
[高雄市]
Published:
撰者;
Year of Publication:
民99[2010]
Description:
73面圖,表 : 30公分;
Subject:
全球定位系統
Subject:
Global Positioning System
Online resource:
http://handle.ncl.edu.tw/11296/ndltd/39956431781068064841
Summary:
近年來全球定位系統(Global Positioning System, GPS)為各界廣泛運用,例如航空飛行、船隻航行到車用導航及測量等。如何再提升其定位精準度,一直以來為各界所努力的目標。GPS定位誤差主要可分為量測誤差與幾何誤差,而其中,幾何精度稀釋因子(Geometric Dilution of Precision, GDOP)是指GPS全球定位系統中,衛星位置幾何分佈優劣的指標。當幾何精度稀釋因子越高,定位誤差越大,反之則定位誤差越小,定位精準度較高。在過去,幾何稀釋精度因子的計算需使用大量的矩陣運算,除了耗費了大量的處理時間外,搭載GPS的行動裝置的電力消耗也是一種負擔。本研究將利用機器學習領域裡,擁有高度預測能力的基因程式規劃(Genetic Programming)來處理GPS衛星之幾何稀釋精度因子之逼近問題,期望能在GPS定位應用中,降低矩陣運算時間,並能維持定位之精準度。本研究提出幾種的迴歸模式並比較其在不同的環境參數下的表現。 Global Positioning System (GPS) has been used extensively in various fields. One key to success of using GPS is the positioning accuracy. Geometric Dilution of Precision (GDOP) is an indicator showing how well the constellation of GPS satellites is organized geometrically. Traditional methods for the calculation of GDOP need to solve the measurement equations with complicated matrix transformation and inversion. GDOP can also be viewed as a regression problem from satellite signals. Previous study employs black-boxed machine learning methods for solving this problem. However, the structure of the regression models obtained from these methods is unknown so that they can not be analyzed extensively. This study employs the technique of genetic expression programming (GEP) for the regression of GPS GDOP. The regression models obtained from GEP have visible structures and can be modified in GPS application software. Several new input types for regression are defined. The experimental results show that GEP can generate precise models for GSP GDOP than other regression methods.
基因表達程式規劃於全球定位系統中幾何精度稀釋因子之逼近 = GPS GDOP Approximation Using Genetic Expression Programming
何, 亞威
基因表達程式規劃於全球定位系統中幾何精度稀釋因子之逼近
= GPS GDOP Approximation Using Genetic Expression Programming / 何亞威撰 - [高雄市] : 撰者, 民99[2010]. - 73面 ; 圖,表 ; 30公分.
參考書目:面.
全球定位系統Global Positioning System
基因表達程式規劃於全球定位系統中幾何精度稀釋因子之逼近 = GPS GDOP Approximation Using Genetic Expression Programming
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近年來全球定位系統(Global Positioning System, GPS)為各界廣泛運用,例如航空飛行、船隻航行到車用導航及測量等。如何再提升其定位精準度,一直以來為各界所努力的目標。GPS定位誤差主要可分為量測誤差與幾何誤差,而其中,幾何精度稀釋因子(Geometric Dilution of Precision, GDOP)是指GPS全球定位系統中,衛星位置幾何分佈優劣的指標。當幾何精度稀釋因子越高,定位誤差越大,反之則定位誤差越小,定位精準度較高。在過去,幾何稀釋精度因子的計算需使用大量的矩陣運算,除了耗費了大量的處理時間外,搭載GPS的行動裝置的電力消耗也是一種負擔。本研究將利用機器學習領域裡,擁有高度預測能力的基因程式規劃(Genetic Programming)來處理GPS衛星之幾何稀釋精度因子之逼近問題,期望能在GPS定位應用中,降低矩陣運算時間,並能維持定位之精準度。本研究提出幾種的迴歸模式並比較其在不同的環境參數下的表現。 Global Positioning System (GPS) has been used extensively in various fields. One key to success of using GPS is the positioning accuracy. Geometric Dilution of Precision (GDOP) is an indicator showing how well the constellation of GPS satellites is organized geometrically. Traditional methods for the calculation of GDOP need to solve the measurement equations with complicated matrix transformation and inversion. GDOP can also be viewed as a regression problem from satellite signals. Previous study employs black-boxed machine learning methods for solving this problem. However, the structure of the regression models obtained from these methods is unknown so that they can not be analyzed extensively. This study employs the technique of genetic expression programming (GEP) for the regression of GPS GDOP. The regression models obtained from GEP have visible structures and can be modified in GPS application software. Several new input types for regression are defined. The experimental results show that GEP can generate precise models for GSP GDOP than other regression methods.
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http://handle.ncl.edu.tw/11296/ndltd/39956431781068064841
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