基于粗集约简的群智能算法的储层识别  

Reservoir Identification of the Swam Intelligence Algorithms Based on Rough Set Reduction

作　　者：袁可红[1,2] 李艳晓[1] 诸克军[2] 

机构地区：[1]洛阳理工学院数理部,河南洛阳471003 [2]中国地质大学管理学院,湖北武汉410074

出　　处：《湖南工业大学学报》2008年第5期46-48,共3页Journal of Hunan University of Technology

基　　金：国家自然科学基金资助项目(70573101)

摘　　要：提出了一种基于粗集约简的粒子群储层识别方法,即应用粗糙集进行属性约简,应用粒子群(PSO)聚类算法对约简和正规化后的数据进行处理。实验表明,约简后的PSO聚类较约简前在识别率上有明显的提高。The unconventional reservoir identification is presented based on rough set attribute reduction and particle swarm optimization（PSO）, which means utilizing the rough set attribute reduction approach to reduce data space and using PSO clustering algorithm to deal with processing normalized data. Experiment shows that identification rate of the unconventional reservoir with reduced attributes is much higher than all feature attributes in PSO clustering algorithm.

关 键 词：粒子群算法 属性约简 粗糙集 聚类 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]