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Linear approximation of Karnik-Mendel type reduction algorithm
conference contribution
posted on 2015-01-01, 00:00 authored by Syed Moshfeq Salaken, Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, D WuKarnik-Mendel (KM) algorithm is the most used and researched type reduction (TR) algorithm in literature. This algorithm is iterative in nature and despite consistent long term effort, no general closed form formula has been found to replace this computationally expensive algorithm. In this research work, we demonstrate that the outcome of KM algorithm can be approximated by simple linear regression techniques. Since most of the applications will have a fixed range of inputs with small scale variations, it is possible to handle those complexities in design phase and build a fuzzy logic system (FLS) with low run time computational burden. This objective can be well served by the application of regression techniques. This work presents an overview of feasibility of regression techniques for design of data-driven type reducers while keeping the uncertainty bound in FLS intact. Simulation results demonstrates the approximation error is less than 2%. Thus our work preserve the essence of Karnik-Mendel algorithm and serves the requirement of low
History
Event
IEEE International Conference on Fuzzy Systems (2015 : Istanbul, Turkey)Series
IEEE International Fuzzy Systems Conference ProceedingsPagination
1 - 6Publisher
IEEELocation
Istanbul, TurkeyPlace of publication
Piscataway, N.J.Start date
2015-08-02End date
2015-08-05ISSN
1544-5615ISBN-13
9781467374286Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, IEEEEditor/Contributor(s)
A Yazici, N Pal, U Kaymak, T Martin, H Ishibuchi, C Lin, J Sousa, B TutmezTitle of proceedings
FUZZ-IEEE 2015: Proceedings of the IEEE International Conference on Fuzzy SystemsUsage metrics
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