中国科学院大连化学物理研究所机构知识库
Advanced  
DICP OpenIR  > 中国科学院大连化学物理研究所  > 期刊论文
题名: Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation
作者: Li, Xiao-li1;  Li, Li-hong2;  Zhang, Bao-lin1;  Guo, Qian-jin3
关键词: Support vector regression ;  Self-adaptive learning based particle swarm optimization ;  Ore grade estimation
刊名: NEUROCOMPUTING
发表日期: 2013-10-22
DOI: 10.1016/j.neucom.2013.03.002
卷: 118, 页:179-190
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology ;  Technology
类目[WOS]: Computer Science, Artificial Intelligence
研究领域[WOS]: Computer Science
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.
关键词[WOS]: GLOBAL OPTIMIZATION ;  PARAMETER ;  ALGORITHM ;  SELECTION ;  KERNEL ;  SVM ;  CLASSIFICATION ;  SPACE
语种: 英语
WOS记录号: WOS:000323693700019
Citation statistics: 
内容类型: 期刊论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/137983
Appears in Collections:中国科学院大连化学物理研究所_期刊论文

Files in This Item:

There are no files associated with this item.


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

Recommended Citation:
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.
Service
 Recommend this item
 Sava as my favorate item
 Show this item's statistics
 Export Endnote File
Google Scholar
 Similar articles in Google Scholar
 [Li, Xiao-li]'s Articles
 [Li, Li-hong]'s Articles
 [Zhang, Bao-lin]'s Articles
CSDL cross search
 Similar articles in CSDL Cross Search
 [Li, Xiao-li]‘s Articles
 [Li, Li-hong]‘s Articles
 [Zhang, Bao-lin]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
  Add to CiteULike  Add to Connotea  Add to Del.icio.us  Add to Digg  Add to Reddit 
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Powered by CSpace