PMID- 26112928
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20160107
LR  - 20151013
IS  - 1879-2022 (Electronic)
IS  - 0019-0578 (Linking)
VI  - 58
DP  - 2015 Sep
TI  - A robust hybrid model integrating enhanced inputs based extreme learning machine 
      with PLSR (PLSR-EIELM) and its application to intelligent measurement.
PG  - 533-42
LID - S0019-0578(15)00143-3 [pii]
LID - 10.1016/j.isatra.2015.06.007 [doi]
AB  - In this paper, a robust hybrid model integrating an enhanced inputs based extreme 
      learning machine with the partial least square regression (PLSR-EIELM) was 
      proposed. The proposed PLSR-EIELM model can overcome two main flaws in the 
      extreme learning machine (ELM), i.e. the intractable problem in determining the 
      optimal number of the hidden layer neurons and the over-fitting phenomenon. 
      First, a traditional extreme learning machine (ELM) is selected. Second, a method 
      of randomly assigning is applied to the weights between the input layer and the 
      hidden layer, and then the nonlinear transformation for independent variables can 
      be obtained from the output of the hidden layer neurons. Especially, the original 
      input variables are regarded as enhanced inputs; then the enhanced inputs and the 
      nonlinear transformed variables are tied together as the whole independent 
      variables. In this way, the PLSR can be carried out to identify the PLS 
      components not only from the nonlinear transformed variables but also from the 
      original input variables, which can remove the correlation among the whole 
      independent variables and the expected outputs. Finally, the optimal relationship 
      model of the whole independent variables with the expected outputs can be 
      achieved by using PLSR. Thus, the PLSR-EIELM model is developed. Then the 
      PLSR-EIELM model served as an intelligent measurement tool for the key variables 
      of the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene 
      (HDPE) process. The experimental results show that the predictive accuracy of 
      PLSR-EIELM is stable, which indicate that PLSR-EIELM has good robust character. 
      Moreover, compared with ELM, PLSR, hierarchical ELM (HELM), and PLSR-ELM, 
      PLSR-EIELM can achieve much smaller predicted relative errors in these two 
      applications.
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 in 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 in 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 in 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 in 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 - 20150622
PL  - United States
TA  - ISA Trans
JT  - ISA transactions
JID - 0374750
OTO - NOTNLM
OT  - Enhanced inputs
OT  - Extreme learning machine
OT  - Intelligent measurement
OT  - Partial least square regression
EDAT- 2015/06/27 06:00
MHDA- 2015/06/27 06:01
CRDT- 2015/06/27 06:00
PHST- 2014/11/19 00:00 [received]
PHST- 2015/04/19 00:00 [revised]
PHST- 2015/06/02 00:00 [accepted]
PHST- 2015/06/27 06:00 [entrez]
PHST- 2015/06/27 06:00 [pubmed]
PHST- 2015/06/27 06:01 [medline]
AID - S0019-0578(15)00143-3 [pii]
AID - 10.1016/j.isatra.2015.06.007 [doi]
PST - ppublish
SO  - ISA Trans. 2015 Sep;58:533-42. doi: 10.1016/j.isatra.2015.06.007. Epub 2015 Jun 
      22.