A nonlinear grade estimation method based on Wavelet Neural Network

Autor:	Li Li-hong, Li Xiaoli, Xie Yuling, Guo Qin-jin
Rok vydání:	2009
Předmět:	Estimation Engineering Wavelet neural network Artificial neural network business.industry Time delay neural network Mathematics::Rings and Algebras Wavelet transform Pattern recognition Machine learning computer.software_genre Nonlinear system Artificial intelligence business computer
Zdroj:	2009 Fourth International on Conference on Bio-Inspired Computing.
DOI:	10.1109/bicta.2009.5338156
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b79468940c06ecf69d98c90720da39e9 https://doi.org/10.1109/bicta.2009.5338156 Zobrazit plný text záznamu