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

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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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  -