测量数据不确定性度量的最小模糊熵算法

Using Minimum Fuzzy Entropy Algorithm to Measure Uncertainty of Geodetic Data

  • 摘要: 测量数据的质量及可靠性取决于测量数据不确定性的大小。从如何评价测量数据的不确定性入手,以测量不确定度理论与模糊数学为基础,构建以测量不确定度为未知参数的测量数据不确定性评价的函数模型,提出“模糊熵测度”作为函数模型求解的最优准则并建立相应的算法,应用高程监测网数据进行解算并与最小二乘估计结果进行比较,结果证明了该方法的可行性。

     

    Abstract: Quality and reliability of geodetic data depend on the size of its uncertainty. In this paper, uncertainty in measurement data is effectivey evaluated based on the theory of measurement uncertainty and fuzzy mathematics, A Function model using measurement uncertainty as unknown parameter is established to directly evaluate the uncertainty of survey data. The Fuzzy Entropy Measure is proposed as the optimal criterion to solve the function model. A corresponding algorithm is established; Experiemental results and comparisons with the least squares estimation show that the proposed method using a elevation monitoring network data solver is feasible.

     

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