A new intelligent prediction method for grade estimation

  • Authors:

  • Xiaoli Li;Yuling Xie;Qianjin Guo

  • Affiliations:

  • Civil & Environmental Engineering School, University of Science and Technology Beijing, Beijing, P.R China;Civil & Environmental Engineering School, University of Science and Technology Beijing, Beijing, P.R China;The State Key Laboratory of Molecular Reaction Dynamics, and Beijing National Laboratory for Molecular Sciences (BNLMS), Institute of Chemistry, Chinese Academy of Sciences, Beijing, P.R China

  • Venue:
  • ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
  • Year:
  • 2010

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Abstract

In this paper, a novel PSO–SVR model that hybridized the constrict particle swarm optimization (PSO) and support vector regression (SVR) is proposed for grade estimation This hybrid PSO–SVR model searches for SVR's optimal parameters using constrict particle swarm optimization algorithms, and then adopts the optimal parameters to construct the SVR models The hybrid PSO–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.