A novel robust regression model based on functional link least square (FLLS) and its application to modeling complex chemical processes. (22nd October 2016)

• Record Type: Journal Article
• Authors: He, Yan-Lin; Zhu, Qun-Xiong
• Is Part Of: Chemical engineering science. Volume 153(2016)
• Page Start: 117
• Page End: 128
• Publication Date: 2016-10-22
• Subjects: Least square -- Functional link artificial neural network -- Modeling -- Purified Terephthalic Acid Process -- High Density Polyethylene process; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2016.07.018
• Languages: English
• ISSNs: 0009-2509

Abstract

Abstract: In this paper, a novel robust regression model is proposed. The proposed robust regression model is called functional link least square (FLLS). The idea of the proposed FLLS model arises from the functional link artificial neural network (FLANN). The FLANN model can be established by using the Error Back-propagation algorithm. However, the performance of the FLANN model is limited. Different from the FLANN model, the proposed FLLS model can achieve an optimal regression model by using the least square algorithm. The proposed FLLS model has some salient features: first, the algorithm of FLLS is extremely fast; secondly, the training errors of the FLLS model can be nearly minimized to be zero; third, the testing performance of FLLS model is robust. In order to evaluate the performance of the proposed regression model, case studies of modeling two complex chemical processes are provided. Two more models of the FLANN and the partial least square (PLSR) are also developed for comparisons. Results illustrated that the proposed FLLS regression model could significantly improve the testing performance. Highlights: A robust functional linked least square (FLLS) regression model is proposed. Least square optimization is adopted to obtain optimal weights. FLLS model has a very fast learning speed. FLLS is developed as soft sensor to predict key process variables. FLLS model could significantly improve the prediction performance.