A new neural network-based type reduction algorithm for interval type-2 fuzzy logic systems

Khosravi, Abbas, Nahavandi, Saeid and Khosravi, Rihanna 2013, A new neural network-based type reduction algorithm for interval type-2 fuzzy logic systems, in FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems, IEEE, Piscataway, N.J., pp. 1-6.

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Title A new neural network-based type reduction algorithm for interval type-2 fuzzy logic systems
Author(s) Khosravi, Abbas
Nahavandi, Saeid
Khosravi, Rihanna
Conference name Fuzzy Systems. IEEE International Conference (2013 : Hyderabad, India)
Conference location Hyderabad, India
Conference dates 7-10 Jul. 2013
Title of proceedings FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE International Conference on Fuzzy Systems
Start page 1
End page 6
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) type reduction
interval type-2 fuzzy logic system
neural network
Summary This 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.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057149

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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