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计算机系统应用英文版:2017,26(5):232-238
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LS-SVM算法在近红外光谱煤质分析中的应用
(解放军后勤工程学院 化学与材料工程系, 重庆 401331)
Application of LS-SVM Algorithm in Coal Quality Indicators Prediction Using Near-Infrared Spectroscopy
(Department of Chemistry and Material Engineering, Logistic Engineering University, Chongqing 401331, China)
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Received:August 11, 2016    Revised:September 18, 2016
中文摘要: 针对目前电力行业煤质分析的需求,提出了基于Hadamard近红外光谱的煤质分析技术,对Hadamard近红外光谱仪研制、控制分析软件设计、煤炭光谱信号采集、指标特征信息提取、定量模型建立五个环节综合考虑,研发了Hadamard近红外煤质分析系统.研究中,对41个不同质量指标的标准煤样进行了定量分析预测,考察了在相同粒径的条件下Hadamard近红外光谱对煤炭指标的预测能力,提出了基于ICA+LS-SVM算法的的煤炭指标预测方法,光谱数据与煤炭指标具有很好的相关性,相关系数普遍在0.9以上,取得了较好预测效果.
Abstract:In order to meet the demand of coal quality analysis in power industry, this paper proposes a new technology of coal analysis based on Hadamard near infrared spectroscopy. On the basis of comprehensive consideration of five aspects, such as development of near infrared spectrometer, the design of control and analysis software, the acquisition of coal spectral signal, the extraction of characteristic information and the establishment of quantitative model, the Hadamard near infrared coal quality analysis system is developed. In the study, 41 standard coal samples with different quality indicators are quantitatively analyzed and predicted. The ability of Hadamard near infrared spectroscopy to predict coal indicators in conditions of same particle size is investigated. Based on LS-SVM algorithm, a coal indicators prediction method is proposed, which has a good correlation with the coal index, and the correlation coefficients is above 0.9.
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基金项目:2015 重庆市基础与前沿研究计划(cstc2015jcyjA100009);2014 解放军后勤工程学院青年基金
引用文本:
王帅,张云佳,王雪梅,乔建仙.LS-SVM算法在近红外光谱煤质分析中的应用.计算机系统应用,2017,26(5):232-238
WANG Shuai,ZHANG Yun-Jia,WANG Xue-Mei,QIAO Jian-Xian.Application of LS-SVM Algorithm in Coal Quality Indicators Prediction Using Near-Infrared Spectroscopy.COMPUTER SYSTEMS APPLICATIONS,2017,26(5):232-238