摘要
针对目前静态软测量建模方法无法反映工业过程动态信息,造成预测模型精度低、鲁棒性差等问题,提出了一种基于最小二乘支持向量机(LS-SVM)和自回归-滑动平均模型(ARMA)的软测量建模方法。首先,建立了基于LS-SVM的软测量模型,利用ARMA模型对预测误差的动态估计,通过增加动态校正环节,实现了对静态模型的动态校正以改善系统动态响应特性。最后将上述方法用于乙烯精馏过程中乙烷浓度的软测量建模,仿真结果表明:与单一使用LSSVM模型相比,该方法具有跟踪性能好、泛化能力强等优点,是一种有效的软测量建模方法。
Because static soft sensor modeling can not reflect the dynamic information of industrial processes, which lead to worse estimation precision and robustness. A dynamic soft sensor modeling based on least square vector machine (LS-SVM) and ARMA time series prediction modeling was presented. A static soft sensor model based on LS-SVM was established firstly, and then dynamic correction in the static model was made by using the dynamic estimation of prediction error in ARMA to improve the dynamic response characteristics. Finally, the proposed LSSVM-ARMA was used to predict the concentration of ethane in ethylene distillation. Simulation indicated that this method featured good approximation and good generalization ability as compared with LSSVM, and could be used in soft sensor.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2010年第2期439-443,共5页
CIESC Journal
基金
国家自然科学基金项目(606253202
60704028)
国家重点基础研究发展计划项目(2009CB320603)
长江学者和创新团队发展计划项目(IRT0721)
高等学校学科创新引智计划项目(B08021)
上海市重点学科建设项目(B504)
上海市科技启明星计划项目(08QA14021)~~