烟草科技2016，Vol.49Issue(9)：50-56,7.DOI:10.16135/j.issn1002-0861.2015.0571
近红外技术结合SaE-ELM用于烤烟烘烤关键参数的在线监测
On-line monitoring of key tobacco flue-curing processing parameters by combining near infrared technology with SaE-ELM
摘要
Abstract
Self-adaptive evolutionary extreme learning machine (SaE-ELM) is a type of single hidden layer feedforward neural network learning algorithm with adaptive differential evolution algorithm to optimize the hidden node parameters. Monitoring key parameters during bulk flue-curing of tobacco is difficult, therefore the dynamic variations of leaf moisture content, chlorophyll (SPAD) and starch in tobacco leaves were monitored by combining near infrared spectroscopy with SaE-ELM and adopting cross validation to select the number of hidden layer nodes. The results showed that comparing with partial least squares (PLS) regression, BP neural network, support vector machine (SVM) regression and extreme learning machine (ELM) quantitative models, SaE-ELM models for moisture, chlorophyll and starch contents had the advantages of automatic parameter optimization, better performance, stronger generalization ability and accurate prediction results, achieving correlation coefficients of 0.931 2, 0.917 6 and 0.916 7, respectively. Near infrared technology combined with SaE-ELM can thus accurately determine the variations of these key parameters during bulk flue-curing of tobacco, which provides technical reference for controlling tobacco bulk curing.关键词
近红外光谱/自适应进化极限学习机(SaE-ELM)/烟叶烘烤/含水率/叶绿素/淀粉Key words
Near infrared spectroscopy/SaE-ELM/Tobacco curing/Moisture content/Chlorophyll/Starch分类
轻工纺织引用本文复制引用
宾俊,范伟,周冀衡,李鑫,梁逸曾,肖志新,刘芮..近红外技术结合SaE-ELM用于烤烟烘烤关键参数的在线监测[J].烟草科技,2016,49(9):50-56,7.基金项目
国家自然科学基金项目“化学建模中若干问题的基础研究”（21275164）；湖南省研究生科研创新项目“烟草烘烤过程近红外光谱在线无损监测及变化规律的研究”（CX2015B237）；中国烟草总公司云南省公司科技项目“密集烤房替代能源综合配套技术研究与推广”（2014YN32）。 （）
