Proceedings of the 3rd International Conference on Mechatronics, Robotics and Automation

A hybrid algorithm based on neural network for DO concentration control

Authors
Ran Zhen, Liang Wang, Xueli Xu, Xiaojing Wu, Chao Si, Han Bai
Corresponding Author
Ran Zhen
Available Online April 2015.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
3rd International Conference on Mechatronics, Robotics and Automation
Part of series
Advances in Computer Science Research
Publication Date
April 2015
ISBN
978-94-62520-76-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmra-15.2015.102How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 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  - https://doi.org/10.2991/icmra-15.2015.102
ID  - Zhen2015/04
ER  -