A Combination Predicting Method Based on Innovation GM(1,1) and RBF

DOI

10.2991/iceem-15.2015.21How to use a DOI?

Keywords

GM(1,1); RBF; spacecraft power predicting

Abstract

Proposed a combination predicting method based on innovation grey theory and neural network to improve the precision of spacecraft power predicting. Established innovation GM(1,1)+RBF combination predicting model with optimal predicting precision according to the spacecraft power seasonal fluctuating. Given embedded combination predicting model and compensated combination predicting model. Contrastive analyzed the performance of those different combination predicting methods. The results show that innovation GM(1,1)+RBF combination predicting model with optimal predicting precision gains the best prediction performance. The method is of application space because it is suitable to other parameters predicting.

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  - Xuhua Wang
AU  - Tianshe Yang
AU  - Mingyang LI
AU  - Jianwei Wang
AU  - Jing Zhao
PY  - 2015/12
DA  - 2015/12
TI  - A Combination Predicting Method Based on Innovation GM(1,1) and RBF
BT  - Proceedings of the 2015 International Conference on Electrical, Electronics and Mechatronics
PB  - Atlantis Press
SP  - 83
EP  - 87
SN  - 2352-5401
UR  - https://doi.org/10.2991/iceem-15.2015.21
DO  - 10.2991/iceem-15.2015.21
ID  - Wang2015/12
ER  -