Fuzzy Pruning Based LS-SVM Modeling Development for a Fermentation Process

Abstract

Due to the complexity and uncertainty of microbial fermentation processes, data coming from the plants often contain some outliers. However, these data may be treated as the normal support vectors, which always deteriorate the performance of soft sensor modeling. Since the outliers also contaminate the correlation structure of the least square support vector machine (LS-SVM), the fuzzy pruning method is provided to deal with the problem. Furthermore, by assigning different fuzzy membership scores to data samples, the sensitivity of the model to the outliers can be reduced greatly. The effectiveness and efficiency of the proposed approach are demonstrated through two numerical examples as well as a simulator case of penicillin fermentation process.