A BP neural network model for predicting the production of a cutter suction dredger

DOI

10.2991/ic3me-15.2015.235How to use a DOI?

Keywords

Cutter Suction Dredger, Back-Propagation, Neural Networks, Prediction, Production

Abstract

In dredging engineering, cutter-suction dredgers are the most widely used dredging equipment in the world. Its production directly determines the efficiency of a dredging project. Therefore, predicting the production of a cutter suction dredger is of considerable importance. This paper presents a BP neural network predictor model. We use Bayesian regularization method to analyze the data from a real cutter suction dredger. Three factors (i.e., the swing speed, the velocity of the hydraulic pipeline transporation, and the work-pressure of the cutter) are considedred in the model to predict the production of the dredger. In addition, we evaluate the proposed model by means of the Matlab neural network toobox.

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  - Jinbao Yang
AU  - Fusheng Ni
AU  - Changyun Wei
PY  - 2015/08
DA  - 2015/08
TI  - A BP neural network model for predicting the production of a cutter suction dredger
BT  - Proceedings of the 3rd International Conference on Material, Mechanical and Manufacturing Engineering
PB  - Atlantis Press
SP  - 1221
EP  - 1226
SN  - 2352-5401
UR  - https://doi.org/10.2991/ic3me-15.2015.235
DO  - 10.2991/ic3me-15.2015.235
ID  - Yang2015/08
ER  -