A local geometrical properties application to fuzzy clustering
Fuzzy Sets and Systems
Partition validity and defuzzification
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Improving probabilities in a fuzzy clustering partition
Fuzzy Sets and Systems
| Hi-index | 7.29 |
In recent years, fuzzy logic techniques have been successfully applied in geodesy problems, in particular to GPS. The aim of this work is to test a fuzzy-logic method with an enhanced probability function as a tool to provide a reliable criteria for weighting scheme for satellite-laser-ranging (SLR) station observations, seeking to optimize their contribution to the precise orbit determination (POD) problem. The data regarding the stations were provided by the International Laser Ranging Service (ILRS), NASA/Crustal Dynamics Data Information System (CDDIS) provided the satellite data for testing the method. The software for processing the data is GEODYN II provided by NASA/Goddard Space Flight Center (GSFC). Factors to be considered in the fuzzy-logic clustering are: the total number of LAGEOS passes during the past 12 months, the stability measure of short- and long-term biases, the percentage of LAGEOS normal points that were accepted in CSR weekly LAGEOS analysis, and the RMS uncertainty of the station coordinates. A fuzzy-logic statistical method allows classifying the stations through a clear 'degree of belonging' to each station group. This degree of belonging translates into a suitable weight to be assigned to each station in the global solution. The first tests carried out showed improvements in the RMS of the global POD solution as well as individual stations, to within a few millimeters. We expect further work would lead to further improvements.