在最小二乘意义下用GNL法拟合了常见的四参数隐函数曲线。
The common curves of implicit function with 4 parameters are fitted in the meaning of least square by means of GNL method.
针对干涉数据质量不同的区域采取不同的展开策略，在最小二乘意义下利用有限元方法最小化不同的代价函数来实现。
Different strategies for regions were taken with different data quality, minimizing different cost functions in a least square sense via the finite element method (FEM).
结果显示不等式限制条件下的最小二乘平差的传统解实际上是基于后验分布模式下的贝叶斯解，而且从方差最小的意义上来讲它不是最优解。
The results show that traditional solution of LSI is actually a Bayes solution based on the mode of the posterior distribution and that it is not the best solution in the sense of variance minimizing.
结果显示不等式限制条件下的最小二乘平差的传统解实际上是基于后验分布模式下的贝叶斯解，而且从方差最小的意义上来讲它不是最优解。
The results show that traditional solution of LSI is actually a Bayes solution based on the mode of the posterior distribution and that it is not the best solution in the sense of variance minimizing.
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