Study on Optimization for Grey Forecasting Model

DOI

10.2991/iea-15.2015.67How to use a DOI?

Keywords

grey theory; GM(1,1) model; data transformation; initial condition; background value; prediction accuracy.

Abstract

In order to improve the prediction accuracy of GM(1,1) model, function transformation was applied to improve the smoothness of the original sequence. Considering affecting of the initial value and the background value selection to forecasting precision of the model, this paper puts forward the idea of optimization value from the three aspects: smoothness, the background value and the initial data sequence at the same time, and obtained the improved GM model. The model was applied to the bearing sleeve wear prediction, and compared with the condition of single models. The simulation rusults show that the improved model has smaller error and higher accuracy, the new model prediction accuracy is above 99.8%, and the validity and practicability of the method is illustrated, which enriches the optimization theory of gray model and broadens the application scope of grey model.

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  - Li Ying
AU  - Tang Min-an
AU  - Liu Tao
PY  - 2015/09
DA  - 2015/09
TI  - Study on Optimization for Grey Forecasting Model
BT  - Proceedings of the AASRI International Conference on Industrial Electronics and Applications (2015)
PB  - Atlantis Press
SP  - 275
EP  - 279
SN  - 2352-5401
UR  - https://doi.org/10.2991/iea-15.2015.67
DO  - 10.2991/iea-15.2015.67
ID  - Ying2015/09
ER  -