化工学报2018，Vol.69Issue(6)：2576-2585,10.DOI:10.11949/j.issn.0438-1157.20171301
基于Gath-Geva算法和核极限学习机的多阶段间歇过程软测量
Soft sensors for multi-stage batch processes based on Gath-Geva algorithm and kernel extreme learning machine
摘要
Abstract
Because batch processes have strong non-linearity, multi-stage, slow time-evolution, and batch-to-batch variation, conventional single prediction model cannot effectively capture characteristics of multi-stage and inter-stage transition. A novel multi-model soft sensor method was proposed on the basis of Gath-Geva clustering and kernel extreme learning machine (KELM). First, principal component analysis (PCA) was used to extract features of input variables. Then, Gath-Geva algorithm was used to classify different operating stages of the batch process and local KELM model was built for each operating stage. For a query sample, every local KELM predictions were calculated and final predictions were obtained by integrating fuzzy membership of each local KELM as weight and its corresponding prediction value. The numeric simulation results on data of penicillin fermentation show that this multi-model approach has more accurate prediction than single model.关键词
软测量/间歇过程/主元分析/核极限学习机/Gath-Geva 算法/遗传算法/模型Key words
soft sensor/batch process/principal component analysis/kernel extreme learning machine/Gath-Geva algorithm/genetic algorithm/modeling分类
信息技术与安全科学引用本文复制引用
张雷,张小刚,陈华..基于Gath-Geva算法和核极限学习机的多阶段间歇过程软测量[J].化工学报,2018,69(6):2576-2585,10.基金项目
国家自然科学基金项目(61672216,61673162). supported by the National Natural Science Foundation of China (61672216, 61673162). （61672216,61673162）
