ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) BASED LOCAL ORDINARY KRIGING ALGORITHM FOR SCATTERED DATA INTERPOLATION

Coskun OEzkan

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ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) BASED LOCAL ORDINARY KRIGING ALGORITHM FOR SCATTERED DATA INTERPOLATION

机译：基于自适应神经模糊推理系统（ANFIS）的本地常规克里格算法进行散射数据插值

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摘要

A new approach to the Ordinary Kriging interpolation method based on the combination of local interpolation and variogram modelling with Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed for scattered data interpolation. In this method, the experimental variogram is modelled by ANFIS and this model is used to interpolate the unknown values of specific points in a new local manner. In this local way, all the unknown points are grouped based on each reference point. The study data obtained from mathematical functions are used. The tests show that the proposed method provides better performances for all data sets in comparison to the well known and highly approved interpolation methods; Ordinary Kriging, Triangle Based Cubic and Radial Basis Function-Multiquadric. Moreover, by the proposed method the computational complexity impressively decreases compared to the global ordinarv Kriging.

机译：提出了一种基于局部插值和变异函数建模与自适应神经模糊推理系统（ANFIS）相结合的普通克里格插值方法，用于离散数据插值。在这种方法中，实验变异函数由ANFIS建模，并且该模型用于以新的局部方式内插特定点的未知值。以这种局部方式，所有未知点均基于每个参考点进行分组。使用从数学函数获得的研究数据。测试表明，与众所周知的和高度认可的插值方法相比，该方法为所有数据集提供了更好的性能。普通克里金法，基于三角形的三次方和径向基函数-二次方。此外，与全局ordinarv Kriging相比，通过提出的方法，计算复杂度显着降低。

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来源
《Survey Review》 |2009年第314期|395-407|共13页
作者
Coskun OEzkan;
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作者单位

Erciyes University, Geodesy and Photogrammetry Engineering Department, 38039, Kayseri, Turkey;

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原文格式 PDF
正文语种 eng
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关键词
kriging; interpolation; variogram modelling; adaptive neuro-fuzzy inference;

机译：克里格插值变异函数建模;自适应神经模糊推理;

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ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) BASED LOCAL ORDINARY KRIGING ALGORITHM FOR SCATTERED DATA INTERPOLATION

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