A Revised Deep Belief Network for Predicting the Slurry Concentration of a Cutter Suction Dredger
- 10.2991/iccia-17.2017.92How to use a DOI?
- Cutter Suction Dredger, Slurry Concentration, Deep Belief Network, Classifier.
In order to predict the slurry concentration of a Cutter Suction Dredger (CSD), a revised Deep Belief Network (DBN) that contains two classifier models is proposed in this work. The two classifier models (i.e., a constant step model and a probability sampling model) are used to process the original data captured in a CSD during a dredging project. Then the classifier models are employed to build the revised DBN to predict the slurry concentration of a CSD. The simulated results show that the proposed approach can effectively extract the features of working data, and also predict the slurry concentration efficiently.
- © 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 - Changyun Wei AU - Fusheng Ni AU - Jinbao Yang PY - 2016/07 DA - 2016/07 TI - A Revised Deep Belief Network for Predicting the Slurry Concentration of a Cutter Suction Dredger BT - Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017) PB - Atlantis Press SP - 547 EP - 553 SN - 2352-538X UR - https://doi.org/10.2991/iccia-17.2017.92 DO - 10.2991/iccia-17.2017.92 ID - Wei2016/07 ER -