A Revised Deep Belief Network for Predicting the Slurry Concentration of a Cutter Suction Dredger

DOI

10.2991/iccia-17.2017.92How to use a DOI?

Keywords

Cutter Suction Dredger, Slurry Concentration, Deep Belief Network, Classifier.

Abstract

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.

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  - 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  -