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 - Personal Name; Christopher E. Ndehedehe - Personal Name; Onuwa Okwuashi - Personal Name; Aniekan E. Eyoh - Personal Name;

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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Detail Information

Series Title: Applied Computing and Geoscience - Open Access
Call Number: 551.136
Publisher: Amsterdam : Elsevier., 2022
Collation: 11 hlm PDF, 4.653 KB
Language: Inggris
ISBN/ISSN: 2590-1974
Classification: 551.136
Content Type: text
Media Type: -
Carrier Type: -
Edition: Vol.14, June 2022
Subject(s): Steepest descent
Geodesy
Coordinate transformation
Minna datum
Specific Detail Info: -
Statement of Responsibility: -

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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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