NDLI: A fuzzy neural network precipitation model established by blurring the rough set factors

Content Provider	IEEE Xplore Digital Library
Author	Long Jin Xvming Shi Ying Huang
Copyright Year	2008
Description	Author affiliation: Res. Inst. of Meteorol. Disaster, Nanning (Long Jin; Xvming Shi)
Abstract	To improve the predictive ability of a fuzzy neural network prediction model, the re-selection is made, by means the rough set attribute reduction, of the correlated prognostic factors that have been chosen and the re-selected factors are treated by blurring as model input, thereby establishing a new-type fuzzy neural network predictive model. Experiments are conducted for approximately two months with day-to-day mean rainfall as the predictive target. Result shows that the presented model that results from a new technique for choosing prognostic factors and a processing scheme is superior to the conventional regression and fuzzy neural network prediction models, leading to appreciably higher precision of results compared to the latter two. Eventually, the merits of the rough set attribute reduction and blurring techniques are explained.
Starting Page	810
Ending Page	813
File Size	119925
Page Count	4
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4593026
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Artificial neural networks Atmospheric modeling Training Fuzzy neural networks Decision making Mathematical model blurring treatment rainfall prediction rough set fuzzy neural network
Content Type	Text
Resource Type	Article

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