Multigrades Classification Model of Magnesite Ore Based on SAE and ELM

Abstract

Magnesite is an important raw material for extracting magnesium metal and magnesium compound; how precise its grade classification exerts great influence on the smelting process. Thus, it is increasingly important to determine fast and accurately the grade of magnesite. In this paper, a method based on stacked autoencoder (SAE) and extreme learning machine (ELM) was established for the classification model of magnesite. Stacked autoencoder (SAE) was firstly used to reduce the dimension of magnesite spectrum data and then neutral network model of extreme learning machine (ELM) was adopted to classify the data. Two improved extreme learning machine (ELM) models were employed for better classification, namely, accuracy extreme learning machine (AELM) and integrated accuracy (IELM) to build up the classification models. The grade classification through traditional methods such as chemical approaches, artificial methods, and BP neutral network model was compared to that in this paper. Results showed that the classification model of magnesite ore through stacked autoencoder (SAE) and extreme learning machine (ELM) is better in terms of speed and accuracy; thus, this paper provides a new way for the grade classification of magnesite ore.