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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/47713

Title: 粒子群演算法求解多天體的天文定位
Particle Swarm Optimization for Solving the Celestial Fix of Multiple Bodies
Authors: Kuo, Pei-Hung
郭倍宏
Contributors: 國立臺灣海洋大學:商船學系
Keywords: 天文航海;天文定位;粒子群演算法;預判機制
Particle Swarm Optimization;Celestial Fix;Overdetermined celestial fix;Multiple bodies
Date: 2016
Issue Date: 2018-08-08T03:41:33Z
Abstract: 因應美國運輸安全委員會(NTSB)建議:「鼓勵開發相對於GPS的備用系統。」,本論文利用粒子群演算法為基礎開發天文定位程式,而其目標為求解多天體定位問題。多天體定位之概念係以雙天體定位為基礎衍伸而出,並引進截距法,以截距之標準差為評估函數做為參考。多天體定位其解集合之合理的呈現應是天文定位及其誤差範圍(CelFix-ER);且為符合航行員實務作業,應有預判機制(PM),本論文提供過定天文定位的散佈圖,刪除觀測誤差較大的天體資訊後重新求解天文定位及其誤差範圍(CelFix-ER)發現在刪除後誤差範圍明顯縮小,因此可判斷為合理的定位,此二準則係為本論文欲求解多天體定位之基礎,目前求解多天體定位的計算方法並無該機制,另外,本論文亦討論天文定位(CelFix)與誤差三角形之關係,發現其定位點與三角形內心的誤差最小,因此應該為三角形之內心,以及提供使用者參考之粒子群演算法的參數設定,藉由比較標準PSO與無記憶性PSO之優劣後,建議使用者以無記憶性PSO求解天文定位問題,得到慣性權重、個體認知學習因子為0,僅依靠群體最佳解進行搜尋,即群體認知學習因子為1.5、粒子數1000,以及迭代次數36供使用者參考;最後,以數個標準計算實例來驗證本論文應用粒子群演算法求解雙天體以及多天體天文定位之準確性與方便性,並以實例展現天文定位及其誤差範圍與預判機制之重要性。 關鍵字:多天體定位、粒子群演算法、天文定位及其誤差範圍、預判機制。
Response to NTSB advised: “encourage to develop another system to be the backup system of GPS.” The developed program is based on particle swarm optimization(PSO), and its target is to solve the celestial fix of multiple bodies. The concept of multiple bodies fix is based on two bodies celestial fix, then introduce intercept method, using the intercept’s standard deviation as evaluation function to be the reference. The reasonable answer of multiple bodies celestial fix is the celestial fix with the error range(CelFix-ER), also, to meet officer's particle demand at sea, prejudge mechanism (PM)are introduce to this thesis. The scatter chart of the overdetermined celestial fix is also represented to help user decided which bodies is biased to canceled and recalculate again. We found that when we delete the bias object and recalculate, the error range significantly reduced, therefore, we can determine the CelFix is reasonable. These two criteria are the foundation of multiple bodies celestial fix; moreover, The relationship between CelFix and cock hat triangle and the parameter of PSO discusses in the thesis. The CelFix in the cock hat triangle should be incentre. By comparing the Standard PSO and Memoryless PSO. We suggest user solving celestial fix problems with Memoryless PSO, and set the parameter of inertia weight, individual learning factor(c1) are given as 0, society learning factor(c2)1.5, the number of particles is 1000, and the iteration number is 25. Finally, several benchmark examples are shown that using PSO to solve the celestial fix problem are straightforward and accurate. Keywords:particle swarm optimization, celestial fix, prejudge mechanism
URI: http://ethesys.lib.ntou.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=G0010371003.id
http://ntour.ntou.edu.tw:8080/ir/handle/987654321/47713
Appears in Collections:[商船學系] 博碩士論文

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