Online Recursive LSSVM Modeling Method Based on FLOO-CV

Aiming at enhancing soft sensor model's generalization ability,an online recursive LSSVM modeling method based on FLOO-CV is presented.In forward learning,an adaptive FLOO-CV prediction error-based threshold without any manual work for updating the model is proposed and FLOO-CV is also utilized in backward learning to delete redundant samples that put minimal influence on the global model,which retains the model's generalization ability maximally.The foregoing scheme is applied to build an industrial polypropylene unit's melt index model.The results indicate that the proposed method can not only improve model's prediction accuracy but also efficiently decrease model′s update frequency.