Hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) and its application to predicting key process variables. (March 2016)

• Record Type: Journal Article
• Authors: He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong
• Is Part Of: ISA transactions. Volume 61(2016:Mar.)
• Page Start: 155
• Page End: 166
• Publication Date: 2016-03
• Subjects: Intelligent measurement -- Functional link neural network -- Partial least square -- Pearson correlation coefficient -- Purified Terephthalic Acid process -- High Density Polyethylene process; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2015.11.019
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: In this paper, a hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) is proposed. Firstly, an improved functional link neural network with small norm of expanded weights and high input–output correlation (SNEWHIOC-FLNN) was proposed for enhancing the generalization performance of FLNN. Unlike the traditional FLNN, the expanded variables of the original inputs are not directly used as the inputs in the proposed SNEWHIOC-FLNN model. The original inputs are attached to some small norm of expanded weights. As a result, the correlation coefficient between some of the expanded variables and the outputs is enhanced. The larger the correlation coefficient is, the more relevant the expanded variables tend to be. In the end, the expanded variables with larger correlation coefficient are selected as the inputs to improve the performance of the traditional FLNN. In order to test the proposed SNEWHIOC-FLNN model, three UCI (University of California, Irvine) regression datasets named Housing, Concrete Compressive Strength (CCS), and Yacht Hydro Dynamics (YHD) are selected. Then a hybrid model based on the improved FLNN integrating with partial least square (IFLNN-PLS) was built. In IFLNN-PLS model, the connection weights are calculated using the partial least square method but not the error back propagation algorithm. Lastly, IFLNN-PLS was developed as an intelligent measurement model for accurately predicting the key variables in the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. Simulation results illustrated that the IFLNN-PLS could significant improve the prediction performance. Highlights: SNEWHIOC-FLNN is proposed to enhance the generalization performance of FLNN. SNEWHIOC-FLNN has relatively small norm of expanded weights and high input–output correlation. Pearson correlation coefficient is used to select the input variables with high correlation. A hybrid model based on the improve FLNN integrating with PLS (IFLNN-PLS) is developed. IFLNN-PLS can serve as an intelligent model to accurately predict key process variables.