本文選題：近紅外光譜　切入點：菱鎂礦　出處：《光譜學與光譜分析》2017年01期

【摘要】：由工業(yè)發(fā)展需求,針對菱鎂礦石礦物含量不同以及分布不均勻而難以判定其品級的情況,提出一種由近紅外光譜技術結合ELM的菱鎂礦石品級分類模型。該模型可以實現(xiàn)菱鎂礦石品級的快速分類。近紅外光譜利用菱鎂礦中不同種類含H基團對近紅外光譜有不同吸收的特性,用來測定菱鎂礦石的成分及其含量,其操作簡便、不破壞樣品、速度快、準確高效。以遼寧省營口市大石橋的菱鎂礦石30組為研究對象,采集菱鎂礦石的近紅外光譜數據樣本30×973。采用主成分分析(PCA)對其進行降維處理,以主元貢獻率大于99.99%而得到10維的特征變量值。建立了ELM算法定量分析數學模型,取20組樣本為訓練樣本(包括6組特級,14組非特),其余10組作為測試樣本(其中4組特級,6組非特),ELM算法模型的隱含層節(jié)點數選取20。為了進一步提高分類效果,提出兩種ELM算法模型的改進:采用循環(huán)模式對傳統(tǒng)ELM的輸入權值和閾值進行尋優(yōu)的精選ELM和在精選ELM基礎上進行集成的集成-精選ELM。并與用人工方法、化學方法和BP神經網絡模型方法對菱鎂礦石樣品品級分類作對比。結果表明:近紅外光譜和ELM菱鎂礦石品級分類模型不論在時間上還是成本上,都具有明顯的優(yōu)勢,且其準確率能夠達到90%以上,為菱鎂礦石品級分類提供了一條新的途徑。
[Abstract]:According to the demand of industrial development, a classification model of magnesite grade based on Near-Infrared Spectroscopy (NIR) combined with ELM is proposed in view of the fact that the content of magnesite is different and the distribution of magnesite is not uniform, so it is difficult to judge the grade of magnesite.The model can realize fast classification of magnesite grade.Near-infrared spectroscopy (NIR) is used to determine the composition and content of magnesite by using different H-containing groups in magnesite with different absorption characteristics. It is easy to operate, does not destroy the sample, is fast, accurate and efficient.Taking 30 groups of magnesite from Dashiqiao, Yingkou City, Liaoning Province as the research object, the near infrared spectrum data of magnesite were collected.The principal component analysis (PCA) was used to reduce the dimensionality, and the characteristic variable value of 10 dimensions was obtained by using the principal component contribution rate greater than 99.99%.The mathematical model of quantitative analysis of ELM algorithm was established. Twenty groups of samples were taken as training samples (including 6 groups of special grade 14 groups of non-special test samples) and the other 10 groups of which were used as test samples.In order to further improve the classification effect, two improved ELM algorithm models are proposed: select ELM, which uses circular mode to optimize the input weights and thresholds of traditional ELM, and integrated ELM based on selected ELM.The classification of magnesite samples is compared with artificial method, chemical method and BP neural network model.The results show that both the near infrared spectrum and the ELM magnesite classification model have obvious advantages in both time and cost, and the accuracy rate can reach more than 90%, which provides a new way for magnesite classification.
【作者單位】： 東北大學資源土木與工程學院;東北大學信息科學與工程學院;
【基金】：國家自然科學基金項目(41371437,61203214) 國家“十二五”科技支撐計劃課題項目(2015BAB15B01)資助
【分類號】：P575;O657.33

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本文編號：1702969