Approach of the hungarian geoid surface with sequence of neural networks

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Volumn 35, Issue , 2004, Pages

  Zaletnyik, P ^a   Volgyesi L ^a   Palancz B ^a  

^a BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS   (Hungary)

Author keywords

Artificial Intelligence; Data Mining; Geodesy; Neural; Surface

Indexed keywords

CHEMICAL ACTIVATION; DATA MINING; ERRORS; ITERATIVE METHODS; POLYNOMIALS;

ACTIVATION FUNCTIONS; DATA REPRESENTATIONS; DEPENDENT VARIABLES; FUNCTION APPROXIMATION PROBLEMS; GAUSSIAN ACTIVATION FUNCTIONS; POLYNOMIAL APPROACH; STANDARD DEVIATION; TRIGONOMETRIC POLYNOMIAL;

RADIAL BASIS FUNCTION NETWORKS;

EID: 84906845803     PISSN: 16821750     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. Kenyeres A. 1999: Phys. Cem. Earth (A), 24, pp. 85-90.
2. Paláncz B., Völgyesi L. 2003: High accuracy data representation via sequence of neural networks, Acta Geod. Geoph. Hung., Vol. 38 (3), pp. 337-343
3. Papp G., Kalmár J. 1996: In: Proceedings of the 7th International Meeting on Alphine Gravimetry, Österreichische Beiträge zu Meteorologie und Geophysik, pp. 95-96.
4. Tóth Gy., Rózsa Sz. 2000: New Datasets and Techniques - an Improvement in the Hungarian Geoid Solution, Paper presented at Gravity, Geoid and Geodynamics Conference, Banf, Alberta, Canada
5. Zaletnyik P. 2003: Neurális hálózatok alkalmazása a geodéziában, MSc. Thesis, Budapest University of Technology and Economics