华东理工大学学报（自然科学版）2017，Vol.43Issue(2)：227-233,7.DOI:10.14135/j.cnki.1006-3080.2017.02.012
基于CBR和SVR的生化需氧量预测模型
Prediction Model for Biochemical Oxygen Demand Based on CBR and SVR
摘要
Abstract
For the problem of monitoring biochemical oxygen demand (BOD) concentration in wastewater treatment process,a case-based reasoning (CBR) prediction model based on support vector regression machine (SVR) is established in this paper.This model is composed of a case retrieval,a case reuse,a SVR revision and a case retention.The SVR revision model is obtained using the SVR training to revise the BOD concentration suggested from the traditional CBR model.The experiment results indicate that the fitting error of this model is lower compared with the support vector machine (SVM),the BP neural network,RBF neural network and the traditional CBR method.The application of SVR can effectively improve the regression performance and the learning ability for a traditional CBR model.关键词
生化需氧量/支持向量回归机/案例推理/案例修正Key words
biochemical oxygen demand/support vector regression/case-based reasoning/case revision分类
信息技术与安全科学引用本文复制引用
严爱军,倪鹏飞,于远航,王普..基于CBR和SVR的生化需氧量预测模型[J].华东理工大学学报（自然科学版）,2017,43(2):227-233,7.基金项目
国家自然科学基金(61374143) （61374143）
北京市自然科学基金(4152010) （4152010）
