Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)

The New Methods of Improving Smooth Degree of Modeling Data Series

Authors
Qiuping Wang1, Jun Zhang
1School of Business Management, Xi’an University of Technolog
Corresponding Author
Qiuping Wang
Available Online October 2007.
DOI
10.2991/iske.2007.144How to use a DOI?
Keywords
Smooth degree, Function transformation, GM(1,1) model, Precision
Abstract

Improving the smooth degree of modeling data series is key factor of grey model’s precision. According to function transformation theory and grey system modeling theory, elementary study on GM(1,1) model based on function ransformation is given, and the methods of linear function transformation and compound function transformation against modeling data series are put forward to improve model’s precision in the paper. It has been proved that the smooth degree of modeling data series after the two new transformations can be improved. The result of practical application demonstrates use of the two new methods.

Copyright
© 2007, 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 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
Series
Advances in Intelligent Systems Research
Publication Date
October 2007
ISBN
978-90-78677-04-8
ISSN
1951-6851
DOI
10.2991/iske.2007.144How to use a DOI?
Copyright
© 2007, 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  - Qiuping Wang
AU  - Jun Zhang
PY  - 2007/10
DA  - 2007/10
TI  - The New Methods of Improving Smooth Degree of Modeling Data Series
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 842
EP  - 848
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.144
DO  - 10.2991/iske.2007.144
ID  - Wang2007/10
ER  -