Abstract
Geoid height determination is one of the important issues of geodesy because of increasing usage of satellite techniques in geodesy. Geoid heights can be calculated using different methods according to the available data. Soft computing methods such as Fuzzy logic and neural networks became so that they are used to solve many engineering problems. Of these soft computing methods, the Fuzzy logic theory and later developments in uncertainty assessment have enabled us to construct more precise models for our requirements. In this study, the effect of the type of the membership function used to construct the Fuzzy model is examined. For this purpose, eight different membership functions were used to construct of these Fuzzy models for the calculation of geoid heights in Istanbul (Turkey). The Fuzzy model results of these were compared with geoid heights obtained by GPS/levelling methods. The fuzzy approximation models were tested on the test points. The results indicated that the Fuzzy model which used the Gaussian membership function is superior compared to other Fuzzy models.
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