Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering

Restraint Method Research for Coupling Random Error Based on High Dimensional Data Set Multiscale Analysis

Authors
X.Y. Zhou, J.Q. Wang, Z.M. Wang, Y.Y. Jiao, X.G. Pan, C.E. Xiao
Corresponding Author
X.Y. Zhou
Available Online July 2015.
DOI
10.2991/aiie-15.2015.61How to use a DOI?
Keywords
coupling error; restraint; high dimensional data set; multiscale; ship attitude
Abstract

For coupling random error, a new method based on multiscale analysis on high dimensional data set is advanced in this paper. It is extends traditional wavelet to high dimensional data set and does multiscale analysis, so that the information in the multi-measure data can be used better. The step of the restraint algorithm is given. Finally, simulated ship attitude data is used to verify the new method. The results show that the three angles in ship attitude are coupling and the method proposed in this paper is valid, which can get a better restraining result than traditional wavelet method.

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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Volume Title
Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
Series
Advances in Intelligent Systems Research
Publication Date
July 2015
ISBN
978-94-62520-70-7
ISSN
1951-6851
DOI
10.2991/aiie-15.2015.61How to use a DOI?
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/).

Cite this article

TY  - CONF
AU  - X.Y. Zhou
AU  - J.Q. Wang
AU  - Z.M. Wang
AU  - Y.Y. Jiao
AU  - X.G. Pan
AU  - C.E. Xiao
PY  - 2015/07
DA  - 2015/07
TI  - Restraint Method Research for Coupling Random Error Based on High Dimensional Data Set Multiscale Analysis
BT  - Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
PB  - Atlantis Press
SP  - 217
EP  - 220
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-15.2015.61
DO  - 10.2991/aiie-15.2015.61
ID  - Zhou2015/07
ER  -