Soft sensor development for the key variables of complex chemical processes using a novel robust bagging nonlinear model integrating improved extreme learning machine with partial least square

2016 ◽  
Vol 151 ◽  
pp. 78-88 ◽  
Author(s):  
YanLin He ◽  
ZhiQiang Geng ◽  
QunXiong Zhu
Keyword(s):  
Extreme Learning Machine ◽  
Nonlinear Model ◽  
Soft Sensor ◽  
Chemical Processes ◽  
Partial Least Square ◽  
Least Square ◽  
Complex Chemical ◽  
Sensor Development ◽  
Key Variables ◽  
Learning Machine

Comparison of Several Extreme Learning Machine Algorithm for Modeling Concrete Compressive Strength

2014 ◽  
Vol 548-549 ◽  
pp. 1735-1738 ◽  
Author(s):  
Jian Tang ◽  
Dong Yan ◽  
Li Jie Zhao
Keyword(s):  
Compressive Strength ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Partial Least Square ◽  
Least Square ◽  
Extreme Learning Machines ◽  
Concrete Compressive Strength ◽  
Learning Machines ◽  
Learning Machine

Modeling concrete compressive strength is useful to ensure quality of civil engineering. This paper aims to compare several Extreme learning machines (ELMs) based modeling approaches for predicting the concrete compressive strength. Normal ELM algorithm, Partial least square-based extreme learning machines (PLS-ELMs) algorithm and Kernel ELM (KELM) algorithm are used and evaluated. Results indicate that the normal ELMs algorithm has the highest modeling speed, and the KELM has the best prediction accuracy. Every method is validated for modeling concrete compressive strength. The appropriate modeling approach should be selected according different purposes.

scholarly journals Quantitative Analysis of Gas Phase IR Spectra Based on Extreme Learning Machine Regression Model

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5535 ◽  
Author(s):  
Tinghui Ouyang ◽  
Chongwu Wang ◽  
Zhangjun Yu ◽  
Robert Stach ◽  
Boris Mizaikoff ◽  
...  
Keyword(s):  
Regression Model ◽  
Extreme Learning Machine ◽  
Chemometric Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Chemical Components ◽  
Reliable Determination ◽  
Proposed Model ◽  
Learning Machine

Advanced chemometric analysis is required for rapid and reliable determination of physical and/or chemical components in complex gas mixtures. Based on infrared (IR) spectroscopic/sensing techniques, we propose an advanced regression model based on the extreme learning machine (ELM) algorithm for quantitative chemometric analysis. The proposed model makes two contributions to the field of advanced chemometrics. First, an ELM-based autoencoder (AE) was developed for reducing the dimensionality of spectral signals and learning important features for regression. Second, the fast regression ability of ELM architecture was directly used for constructing the regression model. In this contribution, nitrogen oxide mixtures (i.e., N2O/NO2/NO) found in vehicle exhaust were selected as a relevant example of a real-world gas mixture. Both simulated data and experimental data acquired using Fourier transform infrared spectroscopy (FTIR) were analyzed by the proposed chemometrics model. By comparing the numerical results with those obtained using conventional principle components regression (PCR) and partial least square regression (PLSR) models, the proposed model was verified to offer superior robustness and performance in quantitative IR spectral analysis.

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