关键词: 支持向量机回归, 神经网络, GPS高程, 高程异常, 似大地水准面

Abstract: Based on the theory of statistics and the principle of Support Vector Machines(SVM), it is put forward that Support Vector Regression(SVR) is applied into the GPS height transformation in mining area to refine the quasi-geoid in mining area. And investigation on the application of Support Vector Regression, polynomial and GA-BP neural network in GPS height transformation are carried out. The test results show that the precision of fitting data of SVR is superior to that of polynomial and GA-BP. Moreover, SVR can effectively overcome many shortcomings existing in neural network, such as difficult topology selection, over-learning, inevitable local extremum etc.

Key words: Support vector regression, Neural network, GPS height, Height anomaly, Quasi-geoid