The impact of data normalization on 2D coordinate transformation using grnn

Abstract

The coordinate transformation has always been a hot topic in the field of geodesy. The artificial neural network (ANN) has been used as an alternative tool to determine the relationship between any two coordinate systems. Construction of an effective neural network depends on the network architecture, learning parameters and normalization technique used. Finding the best data normalization technique is an important step when designing a neural network. This study investigated the performances of eight normalization techniques on two-dimensional (2D) coordinate transformation using a generalized regression neural network (GRNN). The methods examined included the maximize, min-max, median, median-median absolute deviation (median-MAD), mean-mean absolute deviation (mean-MAD), statistical column, tanh, and z-score. Comparisons revealed that the min-max, median-MAD, mean-MAD, tanh, and z-score techniques achieved superior results compared to the other normalization techniques studied. In addition, the GRNN was found to be an effective, feasible and practical tool for 2D coordinate transformation.