DocumentCode :
401734
Title :
Multifactor dynamic rough prediction models methods for complicated system
Author :
Xiao, Zhi ; Lin, Hong-hua ; Zhong, Bo ; Yang, Xiu-tai
Author_Institution :
Coll. of Econ. & Bus. Adm., Chongqing Univ., China
Volume :
3
fYear :
2003
fDate :
2-5 Nov. 2003
Firstpage :
1826
Abstract :
In this paper, as for multifactor prediction of complex systems, a dynamic rough prediction model and method (abbreviated to DRPM) is proposed. This method based on pattern recognition, with the tool of rough set, deals with datum, selects characteristics, reduces factors, draws the typical patterns of factors and prediction indices and the probable description of relevant relation. Thus the model is established. When new information is acquired, the prediction model is modified, so the dynamic prediction model is established which not only avoids the difficulty to set up accurate analysis mathematical models, but also considers the influence of uncertain factors. The instance shows that DRPM is simple, feasible, effective, and of high precision.
Keywords :
pattern recognition; prediction theory; rough set theory; uncertainty handling; complex systems; complicated system; multifactor dynamic rough prediction models; pattern recognition; rough set; uncertain factors; Economic forecasting; Educational institutions; Electronic mail; Mathematical model; Mathematics; Pattern recognition; Power generation economics; Predictive models; Set theory; Vehicle dynamics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN :
0-7803-8131-9
Type :
conf
DOI :
10.1109/ICMLC.2003.1259793
Filename :
1259793
Link To Document :
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