An Evolutionary Method of Neural Network in System Identification

Shuming T. Wang; Chi-Yen Shen; Yu-Ju Chen; Chuo-Yean Chang; Rey-Chue Hwang

doi:doi:10.11648/j.ijiis.20160505.14

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An Evolutionary Method of Neural Network in System Identification

Shuming T. Wang, Chi-Yen Shen, Yu-Ju Chen, Chuo-Yean Chang, Rey-Chue Hwang

Published in International Journal of Intelligent Information Systems (Volume 5, Issue 5)

Received: 20 October 2016 Published: 20 October 2016

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Abstract

This paper presents an evolutionary method for calculating the important degree (ID) of individual input variable of well-trained neural network (NN). The importance of each input variable of neural network could be distinguished in accordance with ID value obtained. In this research, several linear and nonlinear systems’ identifications were firstly studied and simulated. From the simulation results shown, the evolutionary method proposed is quite promising and accurate for the estimation of system’s parameters. In other worlds, the method proposed could be used for data mining in the real applications. In order to verify our inference view, the evaporation process of thin film was studied either. It is a real case of industrial application. Again, the studied results show that the method proposed indeed has the superiority and potential in the area of data mining.

Published in	International Journal of Intelligent Information Systems (Volume 5, Issue 5)
DOI	10.11648/j.ijiis.20160505.14
Page(s)	75-81
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Evolutionary, Important Degree, Neural Network, System Identification

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APA Style

Shuming T. Wang, Chi-Yen Shen, Yu-Ju Chen, Chuo-Yean Chang, Rey-Chue Hwang. (2016). An Evolutionary Method of Neural Network in System Identification. International Journal of Intelligent Information Systems, 5(5), 75-81. https://doi.org/10.11648/j.ijiis.20160505.14

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ACS Style

Shuming T. Wang; Chi-Yen Shen; Yu-Ju Chen; Chuo-Yean Chang; Rey-Chue Hwang. An Evolutionary Method of Neural Network in System Identification. Int. J. Intell. Inf. Syst. 2016, 5(5), 75-81. doi: 10.11648/j.ijiis.20160505.14

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AMA Style

Shuming T. Wang, Chi-Yen Shen, Yu-Ju Chen, Chuo-Yean Chang, Rey-Chue Hwang. An Evolutionary Method of Neural Network in System Identification. Int J Intell Inf Syst. 2016;5(5):75-81. doi: 10.11648/j.ijiis.20160505.14

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@article{10.11648/j.ijiis.20160505.14,
  author = {Shuming T. Wang and Chi-Yen Shen and Yu-Ju Chen and Chuo-Yean Chang and Rey-Chue Hwang},
  title = {An Evolutionary Method of Neural Network in System Identification},
  journal = {International Journal of Intelligent Information Systems},
  volume = {5},
  number = {5},
  pages = {75-81},
  doi = {10.11648/j.ijiis.20160505.14},
  url = {https://doi.org/10.11648/j.ijiis.20160505.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20160505.14},
  abstract = {This paper presents an evolutionary method for calculating the important degree (ID) of individual input variable of well-trained neural network (NN). The importance of each input variable of neural network could be distinguished in accordance with ID value obtained. In this research, several linear and nonlinear systems’ identifications were firstly studied and simulated. From the simulation results shown, the evolutionary method proposed is quite promising and accurate for the estimation of system’s parameters. In other worlds, the method proposed could be used for data mining in the real applications. In order to verify our inference view, the evaporation process of thin film was studied either. It is a real case of industrial application. Again, the studied results show that the method proposed indeed has the superiority and potential in the area of data mining.},
 year = {2016}
}

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TY - JOUR
T1 - An Evolutionary Method of Neural Network in System Identification
AU - Shuming T. Wang
AU - Chi-Yen Shen
AU - Yu-Ju Chen
AU - Chuo-Yean Chang
AU - Rey-Chue Hwang
Y1 - 2016/10/20
PY - 2016
N1 - https://doi.org/10.11648/j.ijiis.20160505.14
DO - 10.11648/j.ijiis.20160505.14
T2 - International Journal of Intelligent Information Systems
JF - International Journal of Intelligent Information Systems
JO - International Journal of Intelligent Information Systems
SP - 75
EP - 81
PB - Science Publishing Group
SN - 2328-7683
UR - https://doi.org/10.11648/j.ijiis.20160505.14
AB - This paper presents an evolutionary method for calculating the important degree (ID) of individual input variable of well-trained neural network (NN). The importance of each input variable of neural network could be distinguished in accordance with ID value obtained. In this research, several linear and nonlinear systems’ identifications were firstly studied and simulated. From the simulation results shown, the evolutionary method proposed is quite promising and accurate for the estimation of system’s parameters. In other worlds, the method proposed could be used for data mining in the real applications. In order to verify our inference view, the evaporation process of thin film was studied either. It is a real case of industrial application. Again, the studied results show that the method proposed indeed has the superiority and potential in the area of data mining.
VL - 5
IS - 5
ER -

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Author Information

Shuming T. Wang

Department of Electrical Engineering, I-Shou University, Kaohsiung City, Taiwan, R.O.C.
Chi-Yen Shen

Department of Electrical Engineering, I-Shou University, Kaohsiung City, Taiwan, R.O.C.
Yu-Ju Chen

Department of Information Management, Cheng Shiu University, Kaohsiung City, Taiwan, R.O.C.
Chuo-Yean Chang

Department of Electrical Engineering, Cheng Shiu University, Kaohsiung City, Taiwan, R.O.C.
Rey-Chue Hwang

Department of Electrical Engineering, I-Shou University, Kaohsiung City, Taiwan, R.O.C.

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APA Style

Shuming T. Wang, Chi-Yen Shen, Yu-Ju Chen, Chuo-Yean Chang, Rey-Chue Hwang. (2016). An Evolutionary Method of Neural Network in System Identification. International Journal of Intelligent Information Systems, 5(5), 75-81. https://doi.org/10.11648/j.ijiis.20160505.14

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ACS Style

Shuming T. Wang; Chi-Yen Shen; Yu-Ju Chen; Chuo-Yean Chang; Rey-Chue Hwang. An Evolutionary Method of Neural Network in System Identification. Int. J. Intell. Inf. Syst. 2016, 5(5), 75-81. doi: 10.11648/j.ijiis.20160505.14

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AMA Style

Shuming T. Wang, Chi-Yen Shen, Yu-Ju Chen, Chuo-Yean Chang, Rey-Chue Hwang. An Evolutionary Method of Neural Network in System Identification. Int J Intell Inf Syst. 2016;5(5):75-81. doi: 10.11648/j.ijiis.20160505.14

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@article{10.11648/j.ijiis.20160505.14,
  author = {Shuming T. Wang and Chi-Yen Shen and Yu-Ju Chen and Chuo-Yean Chang and Rey-Chue Hwang},
  title = {An Evolutionary Method of Neural Network in System Identification},
  journal = {International Journal of Intelligent Information Systems},
  volume = {5},
  number = {5},
  pages = {75-81},
  doi = {10.11648/j.ijiis.20160505.14},
  url = {https://doi.org/10.11648/j.ijiis.20160505.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20160505.14},
  abstract = {This paper presents an evolutionary method for calculating the important degree (ID) of individual input variable of well-trained neural network (NN). The importance of each input variable of neural network could be distinguished in accordance with ID value obtained. In this research, several linear and nonlinear systems’ identifications were firstly studied and simulated. From the simulation results shown, the evolutionary method proposed is quite promising and accurate for the estimation of system’s parameters. In other worlds, the method proposed could be used for data mining in the real applications. In order to verify our inference view, the evaporation process of thin film was studied either. It is a real case of industrial application. Again, the studied results show that the method proposed indeed has the superiority and potential in the area of data mining.},
 year = {2016}
}

Copy | Download