Continuous forecasting of ship sway based on lssvm

DOI

10.2991/icmmse-16.2016.16How to use a DOI?

Keywords

Least square support vector machine, Ship sway, Continuous forecasting

Abstract

Least square support vector machine (LSSVM) algorithm is suitable for the data processing based on finite number of training samples to forecast the unknown data by a nolinear model. It has preponderance for solving the small sample, nonlinearity problems. Without prior information of sea waves and the state equations of ship motions, only using the real measured roll and pitch data ,the LSSVM method is applied to solve the problem of short time series Forecasting. Results show that the method satisfies the need of online forecasting within 15 seconds, and continuous forecasting can be realized by sliding the window.

Copyright

© 2016, 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  - Zhou Bo
PY  - 2016/03
DA  - 2016/03
TI  - Continuous forecasting of ship sway based on lssvm
BT  - Proceedings of the 2016 International Conference on Mechanics, Materials and Structural Engineering
PB  - Atlantis Press
SP  - 91
EP  - 96
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmse-16.2016.16
DO  - 10.2991/icmmse-16.2016.16
ID  - Bo2016/03
ER  -