As for the problem that the estimation precision of soft sensor model is not enough on line in chemical processing

a method of multi-model soft sensor is proposed based on improved clustering and weighted bagging.It improves clustering result by reducing error dividing probability with K-neighbors based on traditional fuzzy C-means clustering

and the training sample set is grouped into several feature sets with correlation analysis.At last

a multi-model is constructed by support vector machines adaptively according to weighted bagging algorithm of ensemble learning.The simulation results show that every feature model is assigned with weight reasonably

and the estimated accuracy of model is improved

and the generalization ability is better.