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Research and application of feature extraction derived functional link neural network

ZHU Qunxiong1,2 ZHANG Xiaohan1,2 GU Xiangbai1,3 XU Yuan1,2 HE Yanlin1,2

(1.College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China 100029)
(2.Engineering Research Center of Intelligent Process Systems Engineering, Ministry of Education, Beijing University of Chemical Technology, Beijing, China 100029)
(3.Sinopec Engineering (Group) Co., Ltd., Beijing, China 100101)

【Abstract】Traditional functional link neural network (FLNN) cannot effectively model multi-dimensional, noisy and strongly coupled data in chemical process. A principal component analysis based FLNN (PCA-FLNN) model was proposed to improve modeling effectiveness. Feature extraction of FLNN function extension block not only removed linear correlations between variables but also selected main components of data, which alleviated the complexity of FLNN learning data. The proposed PCA-FLNN model was used to simulate an UCI Airfoil Self-Noise data and purified terephthalic acid (PTA) production process. The simulation results indicate that PCA-FLNN can achieve faster convergence speed with higher modeling accuracy than traditional FLNN.

【Keywords】 functional link neural network; feature extraction; process modeling; purified terephthalic acid;


【Funds】 National Natural Science Foundation of China (61533003, 61703027) Fundamental Research Funds for the Central Universities (ZY1704, JD1708)

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This Article


CN: 11-1946/TQ

Vol 69, No. 03, Pages 907-912+883

March 2018


Article Outline


  • Introduction
  • 1 Introduction to the related methods
  • 2 PCA-FLNN
  • 3 Results and analysis
  • 4 Conclusions
  • References