Online Mixture of Univariate Linear Regression Models for Adaptive Soft Sensors

This paper proposes a mixture of univariate linear regression models (MULRM) to be applied in time-varying scenarios, and its application to soft sensor problems. Offline and online solutions of MULRM will be obtained using the Expectation-Maximization Algorithm. A forgetting factor will be introduced in the online solution to discount the information of already learned data, so that it can be applied in time varying settings. The solution of the proposed method allows its online and recursive application in any regression problem, without the necessity of storing any past value of data. The recursive solution of the MULRM will then be applied in two time-varying real-world prediction problems. The proposed method is compared with four state of art algorithms. In all the experiments, the proposed method always exhibits the best prediction performance.