RBF Neural Network Soft-Sensor Modeling of Rotary Kiln Pellet Quality Indices Optimized by Biogeography-Based Optimization Algorithm-Reference-Cited by-同舟云学术

RBF Neural Network Soft-Sensor Modeling of Rotary Kiln Pellet Quality Indices Optimized by Biogeography-Based Optimization Algorithm

Published:2015 Issue:1 Volume:48 Page:7-15
ISSN:0021-9592
Container-title:Journal of Chemical Engineering of Japan
language:en
Short-container-title:J. Chem. Eng. Japan / JCEJ

Author:

Wang Jie-sheng¹²,Shen Na-na²,Ren Xiu-dong²,Liu Guan-nan³

Affiliation:

1. National Financial Security and System Equipment Engineering Research Center, University of Science and Technology Liaoning

2. School of Electronic and Information Engineering, University of Science and Technology Liaoning

3. Liaoning Anshan Power Supply Company

Publisher

Society of Chemical Engineers, Japan

Subject

General Chemical Engineering,General Chemistry

Link

https://www.jstage.jst.go.jp/article/jcej/48/1/48_14we135/_pdf

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3. Fan, X. H., H. D. Wang, J. Chen, T. Z. Huang, S. N. Yang, Y. L. Wang and T. Z. Zheng; “Expert System for Sinter Chemical Composition Control Based on Adaptive Prediction,” Transactions of Nonferrous Metals Society of China, 6, 47–50 (1996)

4. Fan, X.-H., L. Yang and X.-L. Chen; “Prediction of Iron Ore Sintering Characters on the Basis of Regression Analysis and Artificial Neural Network,” Energy Procedia, 16, 769–776 (2012)

5. Fernández-Navarro, F., C. Hervás-Martínez, R. Ruiz and J. C. Riquelme; “Evolutionary Generalized Radial Basis Function Neural Networks for Improving Prediction Accuracy in Gene Classification Using Feature Selection,” Appl. Soft Comput., 12, 1787–1800 (2012)

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