基于局部重构融合流形聚类的多模型软测量建模

针对单模型描述复杂非线性对象时估计精度低、泛化能力差的问题，提出了一种基于局部重构融合流形聚类的多模型软测量建模方法。该方法将样本集拆分为多个互不相交的样本子簇，克服异常样本点对聚类结果的影响;以各样本子簇重构线性流形面，将属于同一流形面且相距较近的样本子簇进行融合;采用支持向量机为各个子类建立回归子模型，得到一个基于多个子模型的软测量组合模型。在双酚A生产过程质量指标的软测量建模仿真中验证了该方法的有效性。

Multiple model soft sensor based on local reconstruction and fusion manifold clustering

Using a single model to describe a complex nonlinear object,it usually suffers from low accuracy and poor generalization.A multiple model soft sensor approach is presented based on local reconstruction and fusion manifold clustering.In order to restraining the impacts of outliers to clustering results,the data set is split into several small disjoint sub-clusters.By reconstructing linear manifold level based on every sub-cluster respectively,the sub-clusters which are closer and in the same manifold level are merged.Meanwhile,support vector machine is used to construct regression model in terms of each sub-class and a soft-sensor composed model based on the multiple sub-models is obtained finally.The proposed algorithm is used in a soft sensor modeling for the Bisphenol-A productive process,and the result of simulation shows the effectiveness of the algorithm.