计算机工程与应用2012，Vol.48Issue(20)：51-54,4.DOI:10.3778/j.issn.1002-8331.2012.20.010
基于改进极限学习机的软测量建模方法
Soft sensor modeling based on improved extreme learning machine algorithm
摘要
Abstract
To solve the problem that biomass concentration is difficult to measure directly in the fermentation process, a soft sensor modeling method based on Improved Extreme Learning Machine (IELM) is proposed. The least squares method is combined with the ELM algorithm to calculate the optimal learning parameters. And the training error is used as feedback input to improve the stability and prediction of ELM. In order to further improve the stability of the model, the Lanczos Bidiagonalization(LBD) is used to calculate the output weights. The proposed modeling method is used to construct a novel soft sensor model for the erythromycin fermentation process. Compared with ELM^IRLS-ELM and PL-ELM model, IELM model has higher prediction accuracy and stronger generalization capability.关键词
极限学习机/软测量/双对角化/发酵过程Key words
extreme learning machine/ soft sensor/ Lanczos bidiagonalization/ fermentation process分类
信息技术与安全科学引用本文复制引用
张东娟,丁煜函,刘国海,梅从立..基于改进极限学习机的软测量建模方法[J].计算机工程与应用,2012,48(20):51-54,4.基金项目
国家高技术研究发展计划(863) (No.2007AA04Z179) （863）
中国博士后科学基金(No.20110491359). （No.20110491359）
