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 |
| 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. |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, artificial intelligence |
| 研究领域[WOS] | computer science |
| 关键词[WOS] | global optimization ; parameter ; algorithm ; selection ; kernel ; svm ; classification ; space |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000323693700019 |
| 公开日期 | 2015-11-10 |
| 源URL | [http://159.226.238.44/handle/321008/137983]  |
| 专题 | 大连化学物理研究所_中国科学院大连化学物理研究所 |
| 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：大连化学物理研究所
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