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.
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Li, X., Xie, Y., Guo, Q. (2010). A New Intelligent Prediction Method for Grade Estimation. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_63
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DOI: https://doi.org/10.1007/978-3-642-13318-3_63
Publisher Name: Springer, Berlin, Heidelberg
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