Nonlinear regression method with variable region selection and application to soft sensors

Regions of explanatory variables, X, are attempted to be selected in many fields such as spectral analysis and process control. A genetic algorithm-based wavelength selection (GAWLS) method is one of the methods used to select combinations of important variables from X-variables using regions as a unit of measurement. However, a partial least squares method is used as a regression method, and hence, a GAWLS method cannot handle nonlinear relationship between X and an objective variable, y. We therefore proposed a region selection method based on GAWLS and support vector regression (SVR), one of the nonlinear regression methods. The proposed method is named GAWLS–SVR. We applied GAWLS–SVR to simulation data and industrial polymer process data, and confirmed that predictive, easy-to-interpret, and appropriate models were constructed using the proposed method.

► Regions of explanatory variables (X) are attempted to be selected in many fields.
► A traditional method cannot handle nonlinear relationship between variables.
► Our goal is to select appropriate X-variable regions and construct a nonlinear model.
► We proposed new variable region selection method with support vector regression.
► The performance of the proposed method was confirmed with a variety of data sets.