加权总体最小二乘和RBF神经网络的三维坐标转换

分析部分变量误差加权总体最小二乘法（PWTLS）、加权总体最小二乘法（WTLS）和最小二乘法（LS）在三维坐标转换模型参数求解中的应用与影响，提出PWTLS与RBF神经网络组合的坐标转换方法。结果表明，当三维坐标转换模型系数矩阵中同时存在常数元素和重复元素时，PWTLS方法计算的单位权中误差和内符合精度均优于LS方法，且源坐标改正数较WTLS方法更加合理。PWTLS+RBF组合方法能够使PWTLS的求解参数得到有效使用，提高坐标转换精度。

Three-Dimensional Coordinate Transformation Combined with Weighted Total Least Squares Method and RBF Neural Network

We analyze the application and influence of weighted total least squares method in partial errors-in-variables weight total least squares model (PWTLS), weighted total least squares method(WTLS) and least squares method(LS) in parameters calculation of the three-dimensional coordinate transformation model. Then, we deduce a coordinate transformation method that combines PWTLS and RBF Neural Network. The results show that, when both constant elements and repeated elements exist in the design matrix of coordinate transformation model, unit weighted error and precision of inner coincidence calculated by PWTLS algorithm are better than LS algorithm; in addition, correction of source coordinate calculated by PWTLS is more reasonable than WTLS. The combination method of PWTLS and RBF realizes the practicability of the parameters calculated by PWTLS, which remarkably improves the accuracy of coordinate transformation in practical application.