南京理工大学学报（自然科学版）2017，Vol.41Issue(1)：100-107,8.DOI:10.14177/j.cnki.32-1397n.2017.41.01.014
基于自适应加权最小二乘支持向量机的青霉素发酵过程软测量建模
Soft sensor modeling for penicillin fermentation process based onadaptive weighted least squares support vector machine
摘要
Abstract
The presence of outliers in sample data can corrupt the model'.s performance,giving undesirable results.A novel adaptive weighted least squares support vector machine(AWLS-SVM)regression method is presented for modeling of penicillin fermentation process.In AWLS-SVM,least square support vector machine regression is employed for the sample data to develop model and obtain the sample datum fitting error.According to the fitting error,the adaptive sample weights are obtained via the proposed improved normal distribution weighted scheme.The hybrid chaos differential evolution simulated annealing(CDE-SA)algorithm is proposed to obtain the optimal parameters of the model.The simulation experiment results show that the outliers influencing on the models performance is eliminated in AWLS-SVM,and that the prediction performance is better than those of least squares support vector machine(LS-SVM)and weighted least squares support vector machine(WLS-SVM)method.The AWLS-SVM is applied to develop the soft sensor model for penicillin fermentation process,and the satisfactory result is obtained.关键词
加权最小二乘支持向量机/青霉素发酵过程/正态分布/混沌差分进化-模拟退火优化/软测量建模Key words
weighted least squares support vector machines/penicillin fermentation process/normal distribution function/chaos differential evolution simulated annealing/soft sensor model分类
信息技术与安全科学引用本文复制引用
赵超,李俊,戴坤成,王贵评..基于自适应加权最小二乘支持向量机的青霉素发酵过程软测量建模[J].南京理工大学学报（自然科学版）,2017,41(1):100-107,8.基金项目
国家自然科学基金(60804027 （）
61374133) （）
福州大学科研基金(FZU-022335 （）
600338 （）
600567) （）
高校博士点专项科研基金(20133314120004) （20133314120004）
