Self organized learning applied to global positioning system (GPS) data
Ahmad Nsour, Mohamed Ali Zohdy · 2006
Abstract: – In this paper, we applied self organized unsupervised neural learning to process global navigation systems with multiple input and multiple output signals, we considered versions of topological one-dimensional feature map layer. Several learning methods were examined in order to determine their effect on minimization of the clustering distance errors. New visualization of the multivariable network performance is introduced and applied to sample global positioning system (GPS) data. Although this study is significant, extensions to two-dimensional and higher dimensional layers feature map are currently being developed.