Self organized learning applied to global positioning system (GPS) data

Authors:
Ahmad R. Nsour;Mohamed A. Zohdy
Affiliations:
Department of Electrical & Computer Engineering, Oakland University, Rochester, MI;Department of Electrical & Computer Engineering, Oakland University, Rochester, MI
Venue:
SSIP'06 Proceedings of the 6th WSEAS International Conference on Signal, Speech and Image Processing
Year:
2006

Citing 5
Cited 2

Self-organization and associative memory: 3rd edition

Self-organization and associative memory: 3rd edition
Neural networks for pattern recognition

Neural networks for pattern recognition
Self-Organizing Maps

Self-Organizing Maps
Emergence versus self-organisation: different concepts but promising when combined

Engineering Self-Organising Systems
Self-organization in communication networks: principles and design paradigms

IEEE Communications Magazine

Ionospheric effects on GPS range finding using 3D ray-tracing and Nelder-Mead optimisation algorithm

WSEAS Transactions on Mathematics
Model validation for GPS total electron content (TEC) using 10th polynomial function technique at an equatorial region

WSEAS Transactions on Computers

Quantified Score

Hi-index	0.00

Visualization

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.