Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation
文献类型：期刊论文
| 作者 | Li, Xiao-li1; Li, Li-hong2; Zhang, Bao-lin1; Guo, Qian-jin3 |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2013-10-22 |
| 卷号 | 118页码:179-190 |
| 关键词 | Support Vector Regression Self-adaptive Learning Based Particle Swarm Optimization Ore Grade Estimation |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2013.03.002 |
| 英文摘要 | Ore grade estimation is one of the key stages and the most complicated aspects in mining. Its complexity originates from scientific uncertainty. In this paper, a novel hybrid SLPSO-SVR model that hybridized the self-adaptive learning based particle swarm optimization (SLPSO) and support vector regression (SVR) is proposed for ore grade estimation. This hybrid SLPSO-SVR model searches for SVR's optimal parameters using self-adaptive learning based particle swarm optimization algorithms, and then adopts the optimal parameters to construct the SVR models. The SVR uses the 'Max-Margin' idea to search for an optimum hyperplane, and adopts the e-insensitive loss function for minimizing the training error between the training data and identified function. The hybrid SLPSO-SVR grade estimation method has been tested on a number of real ore deposits. The result shows that method has advantages of rapid training, generality and accuracy grade estimation approach. It can provide with a very fast and robust alternative to the existing time-consuming methodologies for ore grade estimation. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved. |
| 语种 | 英语 |
| WOS记录号 | WOS:000323693700019 |
| 出版者 | ELSEVIER SCIENCE BV |
| 源URL | [http://ir.iccas.ac.cn/handle/121111/42186]  |
| 专题 | 中国科学院化学研究所 |
| 通讯作者 | Guo, Qian-jin |
| 作者单位 | 1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Mineral Resources, Beijing 100029, Peoples R China 2.Henan Univ Sci & Technol, Vehicle & Mot Power Engn Coll, Kaifeng 471023, Henan, Peoples R China 3.Chinese Acad Sci, Inst Chem, State Key Lab Mol React Dynam, Beijing 100080, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Xiao-li,Li, Li-hong,Zhang, Bao-lin,et al. Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation[J]. NEUROCOMPUTING,2013,118:179-190. |
| APA | Li, Xiao-li,Li, Li-hong,Zhang, Bao-lin,&Guo, Qian-jin.(2013).Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation.NEUROCOMPUTING,118,179-190. |
| MLA | Li, Xiao-li,et al."Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation".NEUROCOMPUTING 118(2013):179-190. |
入库方式： OAI收割
来源：化学研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。