摘要
提出了一种模糊信息粒化方法和支持向量机相结合的软测量建模方法.利用模糊信息粒化方法对样本数据进行特征提取,降低样本的维数;利用提取的特征作为支持向量机的输入进行建模.用该方法建立柴油凝点的软测量模型,结果表明,该模型具有很好的预测精度和泛化性能,是一种有效的数据建模方法.
A kind of soft sensing model is proposed by combining methods of fuzzy information granulation（FIG） with support vector machine（SVM）.The FIG has excellent performance of feature extraction and can reduce the dimension of the model sample in input data space.The extracted features are applied to SVM input for proceeding regressive modeling.Above measures are used to establish soft sensing model of diesel oil solidifying point.Results show that this model can get a good estimative accuracy and it is of wide extension as an effective method for data modeling.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2012年第9期955-959,共5页
Transactions of Beijing Institute of Technology
基金
国家自然科学基金资助项目(51104175)
山东省基金资助项目(ZR2011FM014)
关键词
软测量
模糊信息粒化
支持向量机
结构风险最小化
soft sensing
fuzzy information granulation
support vector machine
structural risk minimization
