DICP OpenIR
Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation
Li, Xiao-li1; Li, Li-hong2; Zhang, Bao-lin1; Guo, Qian-jin3
KeywordSupport Vector Regression Self-adaptive Learning Based Particle Swarm Optimization Ore Grade Estimation
Source PublicationNEUROCOMPUTING
2013-10-22
DOI10.1016/j.neucom.2013.03.002
Volume118Pages:179-190
Indexed BySCI
SubtypeArticle
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Artificial Intelligence
WOS Research AreaComputer Science
WOS KeywordGLOBAL OPTIMIZATION ; PARAMETER ; ALGORITHM ; SELECTION ; KERNEL ; SVM ; CLASSIFICATION ; SPACE
AbstractOre 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.
Language英语
WOS IDWOS:000323693700019
Citation statistics
Cited Times:15[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://cas-ir.dicp.ac.cn/handle/321008/137983
Collection中国科学院大连化学物理研究所
Affiliation1.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
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.
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