Estimating the seven transformational parameters between two geodetic datums using the steepest descent algorithm of machine learning

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Estimating the seven transformational parameters between two geodetic datums using the steepest descent algorithm of machine learning

Ikechukwu Kalu - Nama Orang; Christopher E. Ndehedehe - Nama Orang; Onuwa Okwuashi - Nama Orang; Aniekan E. Eyoh - Nama Orang;

This study evaluates the steepest descent algorithm as a tool for root mean square (RMS) error optimization in geodetic reference systems to improve the integrity of transformation. With an initial RMS error estimate of 0.01830m, the negative gradient direction was applied through the steepest optimization leading to a final RMS error estimate of 0.00051m. Using the exact line search mode with a one-point step size of 0.1, we achieved the minimum values in less than sixty iterations, regardless of the slow convergence rate of the steepest descent algorithm.

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Perpustakaan BIG (Eksternal Harddisk) 551.136

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Informasi Detail

Judul Seri: Applied Computing and Geoscience - Open Access
No. Panggil: 551.136
Penerbit: Amsterdam : Elsevier., 2022
Deskripsi Fisik: 11 hlm PDF, 4.653 KB
Bahasa: Inggris
ISBN/ISSN: 2590-1974
Klasifikasi: 551.136
Tipe Isi: text
Tipe Media: -
Tipe Pembawa: -
Edisi: Vol.14, June 2022
Subjek: Steepest descent
Geodesy
Coordinate transformation
Minna datum
Info Detail Spesifik: -
Pernyataan Tanggungjawab: -

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Estimating the seven transformational parameters between two geodetic datums using the steepest descent algorithm of machine learning
This study evaluates the steepest descent algorithm as a tool for root mean square (RMS) error optimization in geodetic reference systems to improve the integrity of transformation. With an initial RMS error estimate of 0.01830m, the negative gradient direction was applied through the steepest optimization leading to a final RMS error estimate of 0.00051m. Using the exact line search mode with a one-point step size of 0.1, we achieved the minimum values in less than sixty iterations, regardless of the slow convergence rate of the steepest descent algorithm.
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