CAJ | 학술논문

针对中药浓缩过程中浓缩液浓度估计问题,提出了支持向量机的软测量建模方法.分析了中药浓缩过程及工艺,选择6个过程变量作为辅助变量,用支持向量回归的方法建立软测量模型,并利用过程数据进行参数寻优和校验.利用优化后的模型对中药浓缩过程浓度进行了预测,通过数据验证了模型的学习性能和泛化性能.结果表明建立的软测量模型实现了对中药浓度较为精确的预测,使用支持向量机方法建模用于小样本学习,计算速度快,具有较强的泛化性.
침대중약농축과정중농축액농도고계문제,제출료지지향량궤적연측량건모방법.분석료중약농축과정급공예,선택6개과정변량작위보조변량,용지지향량회귀적방법건립연측량모형,병이용과정수거진행삼수심우화교험.이용우화후적모형대중약농축과정농도진행료예측,통과수거험증료모형적학습성능화범화성능.결과표명건립적연측량모형실현료대중약농도교위정학적예측,사용지지향량궤방법건모용우소양본학습,계산속도쾌,구유교강적범화성.
Aiming at the problem of the estimating of the concentrated-solution's concentration during Chinese traditional medicine concentration process,a soft sensor modeling method based on support vector machines is proposed.The concentrationprocess and technology of Chinese traditional medicine are analyzed,and six process-variables are chose as auxiliary ones.A soft sensor model is constructed by SVR method,and the parameters are optimized and checked making use of the process-data.The concentration during the Chinese-traditional-medicine concentration process,which is predicted by the optimized model,validates the study-capability and generalization-ability of the model through the data.It turns out that the soft sensor model Call provide an accurate prediction to the concentration of Chinese traditional medicine,and the model which is constructed by support-vector-machines method has advantages of quick computation and better generalization ability if used to small samples-learning.