A hybrid algorithm based on neural network for DO concentration control

DOI

10.2991/icmra-15.2015.102How to use a DOI?

Keywords

nonlinear systems; neural networks; self-organizing map; Hybrid learning; dissolved oxygen concentration control

Abstract

The control of the dissolved oxygen concentration in an aerobic reactor is one of the most important and challenging tasks, because of its strong nonlinearities and large uncertain dynamics. In this paper a hybrid algorithm is used to approach this nonlinear dynamic system using feedforward neural network to solve the DO concentration control problem. This hybrid algorithm uses different learning algorithm separately. The weights connecting the input and hidden layers are firstly adjusted by a self-organized learning procedure, while the weights between hidden and output layers are trained by supervised learning algorithm, such as a gradient descent method. The simulation examples are provided to demonstrate the efficiency of the approach compared with radial basis function neural network.

Copyright

© 2015, 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  - Ran Zhen
AU  - Liang Wang
AU  - Xueli Xu
AU  - Xiaojing Wu
AU  - Chao Si
AU  - Han Bai
PY  - 2015/04
DA  - 2015/04
TI  - A hybrid algorithm based on neural network for DO concentration control
BT  - Proceedings of the 3rd International Conference on Mechatronics, Robotics and Automation
PB  - Atlantis Press
SP  - 514
EP  - 522
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmra-15.2015.102
DO  - 10.2991/icmra-15.2015.102
ID  - Zhen2015/04
ER  -