基于最小二乘支持向量机的组合模型在区域似大地水准面拟合中的应用

针对最小二乘支持向量机（least squares support vector machine，LSSVM）在区域似大地水准面拟合过程中存在较大模型误差的问题，构建LSSVM-Shepard GPS高程异常转换组合模型。采用LSSVM拟合高程异常中的中长波项，利用Shepard插值模型来泛化去除中长波项的残余项。结合平原地区和高原山区工程实例，分别采用二次曲面模型、LSSVM、Shepard插值模型、二次曲面-Shepard模型、LSSVM-Shepard模型进行高程转换及精度对比。结果表明，新组合模型高程转换精度高于各单一模型，且在平原地区转换效果与二次曲面-Shepard模型基本一致，在高原山区拟合效果优于二次曲面-Shepard模型。

Application of Combined Model Based on LSSVM in Regional Quasi-Geoid Fitting

Aiming at the big model error of least squares support vector machine(LSSVM) in regional quasi-geoid fitting, we establish the combination model of LSSVM and Shepard interpolation model for GPS elevation conversion. We use LSSVM to fit the medium-long wave term in abnormal height, and use Shepard interpolation model to generalize and remove the residual of medium-long wave term. Combined with engineering examples in plain area and plateau mountainous area, we use the quadric surface model, the LSSVM, the Shepard interpolation model, the quadratic-Shepard model, and the LSSVM-Shepard model for elevation conversion and accuracy comparison. The results show that the accuracy of new combined model is higher than that of single model. Moreover, the conversion effect of LSSVM-Shepard model in the plain area is basically consistent with the quadratic-Shepard model, and the fitting effect is better than the quadratic-Shepard model in the plateau mountainous area.