Optimizing Extreme Learning Machine Algorithm using Particle Swarm Optimization to Estimate Iron Ore Grade

IF 1.1 Q3 MINING & MINERAL PROCESSING
M. Fathi, A. Alimoradi, H. R. H. Ahooi
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引用次数: 3

Abstract

Scientific uncertainties make the grade estimation very complicated and important in the metallic ore deposits. This paper introduces a new hybrid method for estimating the iron ore grade using a combination of two artificial intelligence methods; it is based on the single layer-extreme learning machine and the particle swarm optimization approaches, and is designed based on the location of the boreholes, depth of the boreholes, and drill hole information from an orebody, and applied for the ore grade estimation on the basis of a block model. In this work, the two algorithms of optimization clustering and neural networks are used for the iron grade estimation in the Choghart iron ore north anomaly in the central Iran. The results of the training and testing the algorithms indicate a significant ability of the optimized neural network system in the ore grade estimation.
基于粒子群优化的极限学习机算法在铁矿石品位估计中的应用
科学的不确定性使金属矿床的品位估算变得非常复杂和重要。本文介绍了一种新的混合方法,将两种人工智能方法相结合来估计铁矿石品位;它基于单层极限学习机和粒子群优化方法,根据矿体的钻孔位置、钻孔深度和钻孔信息进行设计,并应用于基于块体模型的矿石品位估计。本文将优化聚类和神经网络两种算法用于伊朗中部Choghart铁矿北异常的铁品位估计。训练和测试算法的结果表明,优化的神经网络系统在矿石品位估计方面具有显著的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Mining and Environment
Journal of Mining and Environment MINING & MINERAL PROCESSING-
CiteScore
1.90
自引率
25.00%
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