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A Nonlinear Grade Estimation Method Based on Wavelet Neural Network
Li, Xiaoli; Xie, Yuling; Li, Lihong; Guo QJ(郭前进); Li, XL
Department工业信息学研究室
Conference Name4th International Conference on Bio-Inspired Computing
Conference DateOctober 16-19, 2009
Conference PlaceBeijing, China
Author of SourceDalian Univ, Zhengzhou Univ, Light Ind, IEEE Beijing Sect
Source PublicationProceedings of the 2009 Fourth International Conference on Bio-Inspired Computing: Theories and Applications. BIC-TA 2009
PublisherIEEE
Publication PlaceNEW YORK
2009
Pages62-66
Indexed ByEI ; CPCI(ISTP)
EI Accession number20100312635811
WOS IDWOS:000276195100011
Contribution Rank3
ISBN978-1-4244-3865-5
Abstract

Grade estimation is one of the most complicated aspects in mining. Its complexity originates from scientific uncertainty. This paper introduces a nonlinear Wavelet Neural Network (WNN) approach to the problem of ore grade estimation. The nonlinear WNN method combing the properties of the wavelet transform and the advantages of Artificial Neural Networks (ANN) provide fast and reliable ore grade estimation, with minimum assumptions and minimum requirements for modeling skills. The WNN grade estimation method has been tested on a number of real deposits. The result shows that the WNN 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.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/19842
Collection工业信息学研究室
Corresponding AuthorLi, XL
Affiliation1.Civil and Environmental Engineering School, University of Science and Technology, Beijing 100083, China
2.College of Vehicle and Motive Power Engineering, Henna University of Science and Technology, 471003, China
3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
GB/T 7714
Li, Xiaoli,Xie, Yuling,Li, Lihong,et al. A Nonlinear Grade Estimation Method Based on Wavelet Neural Network[C]//Dalian Univ, Zhengzhou Univ, Light Ind, IEEE Beijing Sect. NEW YORK:IEEE,2009:62-66.
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