NDLI: A nonlinear grade estimation method based on Wavelet Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Xiao-li, Li Yu-ling, Xie Li-hong, Li Qin-jin, Guo
Copyright Year	2009
Description	Author affiliation: Shenyang Inst of Automation, Chinese Academy of Sciences, Shenyang 110016, China (Qin-jin, Guo) \|\| Civil & Environmental Engineering School, University of Science and Technology Beijing 100083, China (Xiao-li, Li; Yu-ling, Xie) \|\| College of Vehicle and Motive Power Engineering, Henna University of Science and Technology 471003, China (Li-hong, Li)
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.
Starting Page	1
Ending Page	5
File Size	1001639
Page Count	5
File Format	PDF
ISBN	9781424438662
DOI	10.1109/BICTA.2009.5338156
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Ores Neural networks Neurons Artificial neural networks Educational institutions Intelligent structures Joining processes Automotive engineering Power engineering and energy
Content Type	Text
Resource Type	Article

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