Paláncz, B. and Völgyesi, L. (2003) High accuracy data representation via sequence of neural networks. Acta Geodaetica et Geophysica Hungarica, 38 (3). pp. 337343. ISSN 12178977

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Abstract
Sequence of neural networks has been applied to high accuracy regression in 3D as data representation in form z = f(x,y). The first term of this series of networks estimates the values of the dependent variable as it is usual, while the second term estimates the error of the first network, the third term estimates the error of the second network and so on. Assuming that the relative error of every network in this sequence is less than 100 %, the sum of the estimated values converges to the values to be estimated, therefore the estimation error can be reduced very significantly and effectively. To illustrate this method the geoid of Hungary was estimated via RBF type network. The computations were carried out with the symbolic  numeric integrated system Mathematica.
Item Type:  Article 

Subjects:  Q Science / természettudomány > QE Geology / földtudományok > QE01 Geophysics / geofizika 
Depositing User:  Endre Sarvay 
Date Deposited:  09 Nov 2018 10:19 
Last Modified:  09 Nov 2018 10:19 
URI:  http://real.mtak.hu/id/eprint/87009 
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