中文摘要：

为了减少因煤样粒度而产生的光谱采集误差,研究0.2,1,3和13mm粒度等级下的煤质近红外分析模型。采用PCA方法提取特征信息,建立基于GA-BP和GA-Elman神经网络算法的定量分析模型。实验结果表明,经数据归一化与多元散射校正预处理后,0.2mm粒度等级的光谱与煤炭标准之间的相关性最强,模型的学习精度最高;经平滑处理后1mm粒度等级的分析结果最佳。平滑法对特征谱峰不明显的光谱的预处理效果较差,多元散射校正方法的适用性最强。在0.2mm粒度等级下原光谱的信息准确度最高,1和3mm其次,13mm最差。煤样粒度越大,光谱的不稳定因素越多,从而导致分析模型的负面影响增加。

英文摘要：

In order to reduce the errors of near-infrared spectral acquisition,analytical models of coal spectra with different particle sizes,0.2,1,3 and 13 mm,were studied in this paper.The feature information of spectra was extracted by PCA method,then two quantitative analytical models were established based on GA-BP and GA-Elman neural network algorithms.Through spectral preprocessing with data normalization and multiplicative scatter correction methods,the results showed that with the 0.2 mm size,the correlations between spectra and the standard value were the strongest,and the analytical precision of models were the best.But for smoothed spectra,the models,under 1 mm size,were better than others.Smoothing method was not suitable for the spectra with less obvious wave crest characteristics,while multiplicative scatter correction method was better.According to original spectra,particle size of 0.2 mm had the highest accuracy,followed by 1 and 3 mm and the worst was under 13 mm.Overall,the larger the size for coal particle,the more the unstable factors for spectra,increasing negative influences on analytical models.