NDLI: Decision making for uncertain data in dynamic environment using hybrid method

Content Provider	IEEE Xplore Digital Library
Author	Shahi, A. Atan, R.B. Sulaiman, M.N.
Copyright Year	2009
Description	Author affiliation: UPM University, Serdang, Selangor, Malaysia, 43400 (Shahi, A.; Atan, R.B.; Sulaiman, M.N.)
Abstract	Predicting plays a main role in making a good decision in a dynamic environment with real time data, such as weather predictions that normally are heterogeneous, various and vast. The use of heterogeneous of data available causes increasing uncertainty. Vast amount of data from a dynamic environment will be very uncertain and the prediction of future behavior of such a situation is very hard and if it is possible, the result will not be accurate and may lead to incorrect predictions, Therefore, for the problem statement we propose a new framework that is called hybrid method based on Fuzzy c-mean clustering and Type-2 fuzzy logic system with gradient descent algorithm for training data that would be applicable in a dynamic environment, the hybrid method is able to manage uncertain data such as outliers and noise to get more accurate results and applicable in dynamic environment as well. The results proved the hybrid method is usable and more accurate than the based method (Type-2 FLS).
Starting Page	398
Ending Page	403
File Size	305321
Page Count	6
File Format	PDF
ISBN	9781424447060
DOI	10.1109/ICCA.2009.5410397
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-09
Publisher Place	New Zealand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision making Fuzzy logic Weather forecasting Uncertainty Fuzzy systems Clustering algorithms Training data Environmental management Management training Working environment noise
Content Type	Text
Resource Type	Article

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