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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 Khosravi
A 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.

History

Event

Fuzzy Systems. IEEE International Conference (2013 : Hyderabad, India)

Pagination

1 - 7

Publisher

IEEE

Location

Hyderabad, India

Place of publication

Piscataway, N.J.

Start date

2013-07-07

End date

2013-07-10

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2013, IEEE

Title of proceedings

FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems