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A new neural network-based type reduction algorithm for interval type-2 fuzzy logic systems
conference contribution
posted on 2013-01-01, 00:00 authored by Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, Rihanna KhosraviThis paper introduces a new type reduction (TR) algorithm for interval type-2 fuzzy logic systems (IT2 FLSs). Flexibility and adaptiveness are the key features of the proposed non-parametric algorithm. Lower and upper firing strengths of rules as well as their consequent coefficients are fed into a neural network (NN). NN output is a crisp value that corresponds to the defuzzified output of IT2 FLSs. The NN type reducer is trained through minimization of an error-based cost function with the purpose of improving modelling and forecasting performance of IT2 FLS models. Simulation results indicate that application of the proposed TR algorithm greatly enhances modelling and forecasting performance of IT2 FLS models. This benefit is achieved in no cost, as the computational requirement of the proposed algorithm is less than or at most equivalent to traditional TR algorithms.
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Fuzzy Systems. IEEE International Conference (2013 : Hyderabad, India)Pagination
1 - 6Publisher
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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