|
|||||
|
|
| 基于粗集约简的群智能算法的储层识别 | |
| 引用本文： | 袁可红,李艳晓,诸克军. 基于粗集约简的群智能算法的储层识别[J]. 湖南工业大学学报, 2008, 22(5): 46-48 |
| 作者姓名： | 袁可红 李艳晓 诸克军 |
| 作者单位： | 1. 洛阳理工学院,数理部,河南,洛阳,471003;中国地质大学,管理学院,湖北,武汉,410074 2. 洛阳理工学院,数理部,河南,洛阳,471003 3. 中国地质大学,管理学院,湖北,武汉,410074 |
| 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。 |
| 关 键 词： | 粒子群算法 属性约简 粗糙集 聚类 |
| 收稿时间： | 2008-07-18 |
Reservoir Identification of the Swam Intelligence AlgorithmsBased on Rough Set Reduction |
|
| Yuan Kehong,Li Yanxiao and Zhu Kejun. Reservoir Identification of the Swam Intelligence AlgorithmsBased on Rough Set Reduction[J]. Journal of Hnnnan University of Technology, 2008, 22(5): 46-48 | |
| Authors: | Yuan Kehong Li Yanxiao Zhu Kejun |
| Affiliation: | Department of Mathematic and Physics, Luoyang Institute of Science and Technology;Department of Mathematic and Physics, Luoyang Institute of Science and Technology;School of Management, China University of Geosciences |
| Abstract: | 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. |
| Keywords: | particle swarm optimization attribution reduction rough set clustering |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |
| 点击此处可从《湖南工业大学学报》浏览原始摘要信息 | |
| 点击此处可从《湖南工业大学学报》下载全文 | |