化工学报2018，Vol.69Issue(3)：900-906,封4,8.DOI:10.11949/j.issn.0438-1157.20171435
化工过程深度神经网络软测量的结构与参数自动调整方法
Automatic structure and parameters tuning method for deep neural network soft sensor in chemical industries
摘要
Abstract
Deep learning has been applied to the field of soft sensing in process industries. However, the structure and parameters of deep neural network (DNN) have to be tuned manually, which require solid fundamental knowledge about machine learning and rich experiences on parameters tuning. Complicated tuning procedure restricts generalization application of deep learning in chemical industries. A structure and parameters tuning method for DNN soft sensor with little manual intervention was proposed by systematic analysis on selection process of each essential DNN parameter from massive experiments. The presented method could greatly simplify the tuning procedure and offer a reference for engineers to study and use deep learning. Studies on crude-oil distillation and coal gasification process verified effectiveness and generality of the proposed method.关键词
深度学习/预测/参数调整/算法/神经网络Key words
deep learning/prediction/parameter tuning/algorithm/neural network分类
信息技术与安全科学引用本文复制引用
王康成,尚超,柯文思,江永亨,黄德先..化工过程深度神经网络软测量的结构与参数自动调整方法[J].化工学报,2018,69(3):900-906,封4,8.基金项目
国家自然科学基金项目(61673236,61433001) （61673236,61433001）
欧盟第七框架计划项目(P7-PEOPLE-2013-IRSES-612230).supported by the National Natural Science Foundation of China(61673236,61433001)and the Seventh Framework Program of the European Union(P7-PEOPLE-2013-IRSES-612230). （P7-PEOPLE-2013-IRSES-612230）
