化工学报2017，Vol.68Issue(5)：2009-2015,7.DOI:10.11949/j.issn.0438-1157.20161609
基于双阈值AdaBoost算法的4-CBA含量软测量建模
Modeling soft sensor of 4-CBA concentration by AdaBoost algorithm with dual threshold technique
摘要
Abstract
A modified AdaBoost algorithm with updating sample weight by dual threshold technique was proposed to model a soft sensor for estimating 4-CBA concentration, which could not be measured on-line in PX oxidation process. In this method, weak learners of BP neural networks were trained by part of samples selected by their weights and roulette wheel mechanism. The absolute values of last round training relative errors in weak learners were adopted to update weights of all training samples. Then, a second round updating on sample weights were completed by the product of original sample value and its weighting factor, which was defined by ratio of error range over dual thresholds. In the second updating process, weights were decreased for samples with gross errors but were increased for those with medium error. Consequently, probability of selecting outliers was reduced in following iteration of the training process. Five different methods were applied to model soft sensor of 4-CBA concentration with industrial data. Simulation results showed that the modified AdaBoost algorithm can improve soft sensor performance of 4-CBA concentration with predicting error less than that of other models.关键词
AdaBoost算法/软测量/双阈值/异常样本/4-CBA含量/轮盘赌方法Key words
AdaBoost algorithm/soft sensor/dual threshold technique/outliers/4-CBA concentration/roulette wheel mechanism分类
信息技术与安全科学引用本文复制引用
刘瑞兰,刘树云,戎舟,江兵,庞宗强..基于双阈值AdaBoost算法的4-CBA含量软测量建模[J].化工学报,2017,68(5):2009-2015,7.基金项目
国家自然科学基金项目(61203213)supported by the National Natural Science Foundation of China (61203213). （61203213）
