Title :
A new neural network-based type reduction algorithm for interval type-2 fuzzy logic systems
Author :
Khosravi, Abbas ; Nahavandi, S. ; Khosravi, Rihanna
Author_Institution :
Centre for Intell. Syst. Res., Deakin Univ., Geelong, VIC, Australia
Abstract :
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.
Keywords :
forecasting theory; fuzzy logic; fuzzy neural nets; fuzzy set theory; minimisation; modelling; IT2 FLS models; NN type reducer; TR algorithm; computational requirement; defuzzified output; error-based cost function minimization; forecasting performance; interval type-2 fuzzy logic systems; lower firing strengths; modelling performance; neural network-based type reduction algorithm; nonparametric algorithm; upper firing strengths; Approximation algorithms; Artificial neural networks; Computational modeling; Fuzzy logic; Prediction algorithms; Predictive models; Training; Type reduction; interval type-2 fuzzy logic system; neural network;
Conference_Titel :
Fuzzy Systems (FUZZ), 2013 IEEE International Conference on
Conference_Location :
Hyderabad
Print_ISBN :
978-1-4799-0020-6
DOI :
10.1109/FUZZ-IEEE.2013.6622361