化工学报2018，Vol.69Issue(6)：2551-2559,封3,10.DOI:10.11949/j.issn.0438-1157.20171286
基于集成学习的多产品化工过程软测量建模方法
Ensemble learning-based soft sensor method for multi-product chemical processes
摘要
Abstract
To handle characteristics of nonlinearity, time-variation and multi-product of chemical processes, a self-adaptive soft sensing method was developed under ensemble learning framework. Initially, a self-adaptive localization technique was proposed to construct ensemble of high diversified local models by statistical hypothesis testing theory and k-nearest neighbor method. Subsequently, based on generalization capabilities of quantified local models with online query sample, primary process variables were estimated through selective ensemble learning. Furthermore, in order to measure estimation accuracy of primary process variables, a highly universal method of model performance assessment was presented by using local model's generalization error. Simulation study on a penicillin fermentation process demonstrated effectiveness of the proposed method.关键词
化工过程/多产品/软测量/集成学习/模型性能评价Key words
chemical processes/multi-product/soft sensor/ensemble learning/model performance assessment分类
信息技术与安全科学引用本文复制引用
邵伟明,田学民,宋执环..基于集成学习的多产品化工过程软测量建模方法[J].化工学报,2018,69(6):2551-2559,封3,10.基金项目
国家重点研发计划重点专项项目(2017YFB0304203) （2017YFB0304203）
国家自然科学基金项目(61703367).supported by the National Key Research and Development Program of China (2017YFB0304203) and the National Natural Science Foundation of China (61703367). （61703367）
