Proceedings of the 3rd Workshop on Advanced Research and Technology in Industry (WARTIA 2017)

Nonlinear Modeling Method Based on RBF Neural Network Trained by AFSA with Adaptive Adjustment

Authors
Xu-Sheng Gan, Zhi-bin Chen, Ming-gong Wu
Corresponding Author
Xu-Sheng Gan
Available Online November 2017.
DOI
10.2991/wartia-17.2017.65How to use a DOI?
Keywords
Radial basis function; Neural network; Artificial fish swarm algorithm; Nonlinear function
Abstract

To improve the nonlinear modeling capability of RBF neural network, an Artificial Fish Swarm Algorithm (AFSA) training algorithm with an adaptive mechanism is proposed. In the training algorithm, the search step size and visible domain of AFSA algorithm can be adjusted dynamically according to the convergence characteristics of artificial fish swarm, and then the improved AFSA algorithm is used to optimize the parameters of RBF neural network. The example shows that, the proposed model is a better approximation performance for the nonlinear function.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 3rd Workshop on Advanced Research and Technology in Industry (WARTIA 2017)
Series
Advances in Engineering Research
Publication Date
November 2017
ISBN
978-94-6252-409-5
ISSN
2352-5401
DOI
10.2991/wartia-17.2017.65How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xu-Sheng Gan
AU  - Zhi-bin Chen
AU  - Ming-gong Wu
PY  - 2017/11
DA  - 2017/11
TI  - Nonlinear Modeling Method Based on RBF Neural Network Trained by AFSA with Adaptive Adjustment
BT  - Proceedings of the 3rd Workshop on Advanced Research and Technology in Industry (WARTIA 2017)
PB  - Atlantis Press
SP  - 336
EP  - 340
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-17.2017.65
DO  - 10.2991/wartia-17.2017.65
ID  - Gan2017/11
ER  -