A Research of GPS Height Fitting in Mountainous Terrain by CPSO Optimization FLS-SVM
2013 ◽
Vol 336-338
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pp. 2339-2343
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Keyword(s):
For the problem of data limited in the mountainous area, a method of FLS-SVM (Fuzzy Least Square Vector Machine) that supporting small sample data and having high noise ability was put forward. The CPSO(chaos particle swarm optimization algorithm) is adopted to optimize the parameters of least squares support vector machine algorithm, and to avoid the uncertainty of artificial parameter selection. Meanwhile, considering the impact of terrain, the terrain correction is introduced to the support vector machine model. The experimental results show that the model can get higher precision fitting effect compared with traditional fitting method such as PSO-LSSVM and GA-LSSVM, and suitable for the SRTM application of getting normal height.
2020 ◽
Vol 19
(6)
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pp. 2075-2090
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Keyword(s):
2015 ◽
Vol 2015
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pp. 1-7
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2012 ◽
Vol 55
◽
pp. 357-365
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2014 ◽
Vol 602-605
◽
pp. 3333-3337
Keyword(s):
2013 ◽
Vol 860-863
◽
pp. 1510-1516
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2013 ◽
Vol 791-793
◽
pp. 912-916
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2012 ◽
Vol 241-244
◽
pp. 1719-1723