Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)

Calculation of Ship Collision Risk Index Based on Adaptive Fuzzy Neural Network

Authors
Chunze Li, Wei Li, Jun Ning
Corresponding Author
Chunze Li
Available Online July 2018.
DOI
https://doi.org/10.2991/msam-18.2018.47How to use a DOI?
Keywords
adaptive fuzzy neural network; BP neural network; ship collision risk Index
Abstract

This paper combines adaptive fuzzy system and neural network to construct an adaptive fuzzy neural network. Using DCPA, TCPA raw data, speed (V), heading (C), angle (Q), distance (D) as the training input of the network, and comparing the learning results of BP neural network and adaptive fuzzy neural network to predict Ship collision risk index. The results show that the adaptive fuzzy neural network has good prediction results.

Copyright
© 2018, 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/).

Download article (PDF)

Volume Title
Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)
Series
Advances in Intelligent Systems Research
Publication Date
July 2018
ISBN
978-94-6252-566-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/msam-18.2018.47How to use a DOI?
Copyright
© 2018, 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  - Chunze Li
AU  - Wei Li
AU  - Jun Ning
PY  - 2018/07
DA  - 2018/07
TI  - Calculation of Ship Collision Risk Index Based on Adaptive Fuzzy Neural Network
BT  - Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)
PB  - Atlantis Press
SP  - 223
EP  - 227
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-18.2018.47
DO  - https://doi.org/10.2991/msam-18.2018.47
ID  - Li2018/07
ER  -