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Evaluation and comparison of type reduction algorithms from a forecast accuracy perspective
conference contribution
posted on 2013-01-01, 00:00 authored by Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, Rihanna KhosraviA variety of type reduction (TR) algorithms have been proposed for interval type-2 fuzzy logic systems (IT2 FLSs). The focus of existing literature is mainly on computational requirements of TR algorithm. Often researchers give more rewards to computationally less expensive TR algorithms. This paper evaluates and compares five frequently used TR algorithms from a forecasting performance perspective. Algorithms are judged based on the generalization power of IT2 FLS models developed using them. Four synthetic and real world case studies with different levels of uncertainty are considered to examine effects of TR algorithms on forecasts accuracies. It is found that Coupland-Jonh TR algorithm leads to models with a better forecasting performance. However, there is no clear relationship between the width of the type reduced set and TR algorithm.
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Fuzzy Systems. IEEE International Conference (2013 : Hyderabad, India)Pagination
1 - 7Publisher
IEEELocation
Hyderabad, IndiaPlace of publication
Piscataway, N.J.Start date
2013-07-07End date
2013-07-10Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2013, IEEETitle of proceedings
FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy SystemsUsage metrics
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