一种基于模糊逻辑的显式解集群技术
Khan Muhammad1, Hylke J Glass2
1Intelligent Information Processing Lab, National Centre of Artificial Intelligence and Department of Mining Engineering, University of Engineering and Technology Peshawar 25000, Pakistan.
Heliyon
|July 24, 2023
概括
这项研究引入了一种用于地质科学资源估计的新型分类技术. 它通过在集群样本中考虑空间和属性相似性来提高准确性,从而导致不偏见的统计分布.
科学领域:
- 地质科学 地质科学
- 地质统计学 在地质统计学
- 数据科学数据科学数据科学
背景情况:
- 地理科学资源的空间估计依赖于准确的统计分布.
- 优选抽样导致资源估计中的参数偏差.
- 传统的分类方法忽略了空间集群中的属性相似性.
研究的目的:
- 开发一种分类技术,以考虑空间和属性相似性.
- 提高资源估计中的统计分布的准确性.
- 为条件模拟和不确定性建模提供一种公正的方法.
主要方法:
- 模糊c-means算法用于将样品分类为空间和地化学集群.
- 基于Mamdani的模糊推理系统,用于导出分类权重.
- 在GSLib和沃克湖数据集上的应用和验证.
主要成果:
- 拟议的分类技术明确考虑了空间和属性分类.
- 模糊的聚类和模糊的推理系统有效地获得了分类权重.
- 这种新方法与传统的细胞分类相比,显示出更高的准确性.
结论:
- 开发的分类方法通过解决样本属性相似性来提高资源估计的准确性.
- 这种方法提供了一种更强大的方法来模拟空间分布的地质科学变量中的不确定性.
- 该技术比传统的分类方法提供了显著的改进.
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