基于混合SVR-PL S 方法的丙烯腈收率软测量建模

为了更有效地处理过程非线性、多输入和数据共线性等复杂特性，提高模型的推广能力和精度，提出了混合支持向量回归机-偏最小二乘法(SVR—PLS)方法．该方法兼具SVR和PLS的优点．用PLS进行特征提取．用SVR建立PLS的内部模型．对工业丙烯腈生产过程丙烯腈收率软测量建模的应用表明．采用该方法建立的软测量模型．在模型精度、推广能力等方面明显优于一些传统软测量建模方法．满足工业应用要求．

Soft sensor modeling of acrylonitrile yield based on hybrid SVR-PLS approach

A hybrid SVR-PLS method is proposed to deal with complicated process with nonlinearity and a large number of correlated inputs. The SVR-PLS method, which has merits of both SVRs and PLS, is an integration of support vector regression machine and partial least squares. The PLS outer projection is used as a dimension reduction tool to remove collinearity and the SVRs are trained to capture the nonlinearity in the projected latent space. Soft sensor modeling of acrylonitrile yield is established using SVR-PLS method. The generalization ability and accuracy of the soft sensor using the method proposed is superior to traditional methods.