区域似大地水准面确定的最小二乘支持向量机方法

支持向量机(SVM)是近年来发展起来的机器学习的新方法,它较好地解决小样本、非线性、高维数、局部极小点等实际问题.文中研究支持向量机的拓展算法--最小二乘支持向量机(LSSVM),并将其应用于确定大面积复杂似大地水准面.通过工程实例并与神经网络模型和二次曲面多项式拟合模型相比较,验证确定区域似大地水准面的LSSVM方法的有效性.

Least squares support vector machine method of area quasi-geoid determination

Support vector machine(SVM) is a novel machine learning method,which is powerful for the problem characterized by small sample,nonlinearity,high dimension and local minima,and has high generalization.In this paper,its continuation algorithm-least squares support vector machine(LSSVM) is studied,then LSSVM algorithm is applied to determining large area complex quasi-geoid.Through taking an example and comparing with neural network model and conicoid polynomial fitting model,the availability of LSSVM method of area quasi-geoid determination is validated.