Abstract
The rapidity of ore grade identification is key to speeding up the beneficiation process in the mining process. Traditional ore grade detection mostly relies on chemical methods. Although these methods have high accuracy, they take a long time, and the cost of detection has always been high. Therefore, this paper proposes a detection method for ore grade using visible-infrared spectroscopy and an incremental two hidden layer extreme learning machine with variable hidden layer nodes based on the truncated singular value decomposition (TSVD-IVTELM) algorithm. Firstly, the spectral data of each sample are obtained by spectrometer. Then, Monte Carlo cross-validation is used to eliminate abnormal samples, and partial least squares regression is used to extract the latent variables of the spectral data to reduce the data dimension. Finally, TSVD-IVTELM is used for regression analysis. TSVD-IVTELM is proved to have the smallest root mean square error and best fitting performance after comparison experiments.
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