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/).
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 -