| 基于约束粒子群优化的克里金插值算法 |
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| 引用本文： | 贾雨,邓世武,姚兴苗,蔡元菲.基于约束粒子群优化的克里金插值算法[J].成都理工大学学报(自然科学版),2015(1):104-109. |
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| 作者姓名： | 贾雨 邓世武 姚兴苗 蔡元菲 |
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| 作者单位： | 1. 成都理工大学 核技术与自动化工程学院,成都,610059;2. 电子科技大学 通信与信息工程学院,成都,611731 |
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| 基金项目： | 国家自然科学基金资助项目(41104067)。 |
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| 摘 要： | 针对常规克里金插值算法中的不足之处,通过改变粒子群算法中粒子多样性,结合地质变量的特征和数据特征,提出了一种改进的插值方法——基于约束粒子群优化的克里金插值算法,在粒子群优化过程中,通过高斯变异、样本点权重系数设定、搜索范围约束等方式提高了插值精度。实验结果表明:基于约束粒子群优化的克里金插值算法可以获得高精度的插值效果,优于常规的克里金插值。
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| 关 键 词： | 克里金插值 约束粒子群算法 变差函数 拟合 |
Kriging interpolation algorithm based on constraint particle swarm optimization |
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| JIA Yu,DENG Shi-wu,YAO Xing-miao,CAI Yuan-fei.Kriging interpolation algorithm based on constraint particle swarm optimization[J].Journal of Chengdu University of Technology: Sci & Technol Ed,2015(1):104-109. |
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| Authors: | JIA Yu DENG Shi-wu YAO Xing-miao CAI Yuan-fei |
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| Abstract: | According to the deficiency of the conventional Kriging interpolation algorithm,this paper proposes an improved interpolation method,that is,the Kriging interpolation algorithm based on the constraint particle swarm optimization (PSO)by changing the diversity of the particles in the PSO and combined with the characteristics of the geological variables and the data features.This method improves the precision of interpolation by means of Gaussian variation,setting the weight coefficient of sample points and limiting the search scope in the process of PSO.The experiment result indicates that the Kriging interpolation algorithm based on the constraint PSO can obtain a high-precision interpolation result superior to that of the conventional Kriging interpolation algorithm. |
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| Keywords: | Kriging interpolation constraint particle swarm optimization variation function fitting |
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