A hybrid algorithm based on neural network for DO concentration control
Ran Zhen, Liang Wang, Xueli Xu, Xiaojing Wu, Chao Si, Han Bai
Available Online April 2015.
- https://doi.org/10.2991/icmra-15.2015.102How to use a DOI?
- nonlinear systems; neural networks; self-organizing map; Hybrid learning; dissolved oxygen concentration control
- 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.
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 - 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 - https://doi.org/10.2991/icmra-15.2015.102 ID - Zhen2015/04 ER -