Soft Sensor Modeling Using SVR Based on Genetic Algorithm and Akaike Information Criterion

Support vector regression (SVR) is one of the new methods of soft sensor modeling for estimating the products of metabolism in microorganism fermentations. The accuracy of SVR is mainly impacted by two factors: input variables selection and parameters set in SVR training procedures. But it is difficult to select the input variables and set the parameters. A novel method of soft sensor modeling is proposed based on akaike information criterion (AIC) and genetic algorithm (GA) to overcome the difficulties. Moreover, a real experiment process-erythromycin fermentation process is used to evaluate the performance of the proposed soft sensor modeling method. Results show the accuracy of the estimation is improved and the number of the input variables is reduced by the proposed approach, and the presented method could have a promising application in industrial process.