PMID- 26685746
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20160712
LR  - 20180415
IS  - 1879-2022 (Electronic)
IS  - 0019-0578 (Linking)
VI  - 61
DP  - 2016 Mar
TI  - 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.
PG  - 155-166
LID - S0019-0578(15)00295-5 [pii]
LID - 10.1016/j.isatra.2015.11.019 [doi]
AB  - 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.
CI  - Copyright (c) 2015 ISA. Published by Elsevier Ltd. All rights reserved.
FAU - He, Yan-Lin
AU  - He YL
AD  - College of Information Science & Technology, Beijing University of Chemical 
      Technology, Beijing 100029, China; Engineering Research Center of Intelligent 
      PSE, Ministry of Education of China, Beijing 100029, China.
FAU - Xu, Yuan
AU  - Xu Y
AD  - College of Information Science & Technology, Beijing University of Chemical 
      Technology, Beijing 100029, China; Engineering Research Center of Intelligent 
      PSE, Ministry of Education of China, Beijing 100029, China.
FAU - Geng, Zhi-Qiang
AU  - Geng ZQ
AD  - College of Information Science & Technology, Beijing University of Chemical 
      Technology, Beijing 100029, China; Engineering Research Center of Intelligent 
      PSE, Ministry of Education of China, Beijing 100029, China.
FAU - Zhu, Qun-Xiong
AU  - Zhu QX
AD  - College of Information Science & Technology, Beijing University of Chemical 
      Technology, Beijing 100029, China; Engineering Research Center of Intelligent 
      PSE, Ministry of Education of China, Beijing 100029, China. Electronic address: 
      zhuqx@mail.buct.edu.cn.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20151210
PL  - United States
TA  - ISA Trans
JT  - ISA transactions
JID - 0374750
OTO - NOTNLM
OT  - Functional link neural network
OT  - High Density Polyethylene process
OT  - Intelligent measurement
OT  - Partial least square
OT  - Pearson correlation coefficient
OT  - Purified Terephthalic Acid process
EDAT- 2015/12/22 06:00
MHDA- 2015/12/22 06:01
CRDT- 2015/12/22 06:00
PHST- 2015/01/04 00:00 [received]
PHST- 2015/09/27 00:00 [revised]
PHST- 2015/11/19 00:00 [accepted]
PHST- 2015/12/22 06:00 [entrez]
PHST- 2015/12/22 06:00 [pubmed]
PHST- 2015/12/22 06:01 [medline]
AID - S0019-0578(15)00295-5 [pii]
AID - 10.1016/j.isatra.2015.11.019 [doi]
PST - ppublish
SO  - ISA Trans. 2016 Mar;61:155-166. doi: 10.1016/j.isatra.2015.11.019. Epub 2015 Dec 
      10.