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- W1593636699 abstract "With some of geodesy’s tasks, it is crucial to have precise knowledge of height reference surfaces and of local geoid respectively. It is impossible to define geoid’s surface by using simple mathemtical functions due to the following reasons: geoid is defined through gravity potential, which is not a directly measurable quantity itself and furthermore, geoid’s curve is subjected to a constant change, depending on the factors of relief change and density of Earth’s interior. Geoid’s surface can be represented by a multitude of discrete points on the one hand and with transformation of these points into a function or a math series on the other. Approximation with the use of artificial neuron networks is one of the recent methods, which is also demonstrated in this task. Theoretical knowledge of relations between geoid heights is not necessary for the purpose of using artificial neuron networks, since the latter absorb these relations on the basis of sufficient number of incoming (geographic latitude and longitude) and outgoing information (geoid height); moreover, networks in question can also predict accurate output values for incoming information, which were not involved in the process of learning. Three different artificial neuron networks are used for the purpose of approximating height reference surface: Kohonen’s counter-propagation artificial neural network, Levenberg-Marquardt’s artificial neural network and the radial basis artificial neural network. Computer programs have been designed for every artificial neural network. The results of the artificial neural networks have been compared and analyzed in respect to four different samples combinations of study and test points (25/100, 50/75, 75/50, 99/26) on the territory of the land Baden-Wurttemberg. The assignment was brought to conclusion by comparing and analyzing the obtained results with the results of previous research on this field." @default.
- W1593636699 created "2016-06-24" @default.
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- W1593636699 date "2007-12-19" @default.
- W1593636699 modified "2023-09-27" @default.
- W1593636699 title "Approximation of height reference surface by artificial neural networks" @default.
- W1593636699 hasPublicationYear "2007" @default.
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