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 integrity of transformation. With an initial RMS estimate 0.01830m, negative gradient direction was applied through leading final 0.00051m. Using exact line search mode with one-point step size 0.1, we achieved minimum values less than sixty iterations, regardless slow convergence rate algorithm.