A class of interval type-2 fuzzy neural networks illustrated with application to non-linear identification

Author

Castro, Juan R. ; Castillo, Oscar

Author_Institution

Div. of Grad. Studies & Res., Baja California Autonomous Univ., Tijuana, Mexico

fYear

2013

fDate

4-9 Aug. 2013

Firstpage

1

Lastpage

6

Abstract

Neural Networks (NN), Type-1 Fuzzy Logic Systems (T1FLS) and Interval Type-2 Fuzzy Logic Systems (IT2FLS) are universal approximators, they can approximate any non-linear function. Recent research shows that embedding T1FLS on an NN or embedding IT2FLS on an NN can be very effective for a wide number of non-linear complex systems, especially when handling imperfect information. In this paper we show that an Interval Type-2 Fuzzy Neural Network (IT2FNN) is a universal approximator with some precision using a set of rules and Interval Type-2 membership functions (IT2MF) and the Stone-Weierstrass Theorem.

Keywords

approximation theory; fuzzy neural nets; nonlinear functions; IT2FLS; Stone-Weierstrass theorem; T1FLS; interval type-2 fuzzy neural networks; nonlinear function; nonlinear identification; type-1 fuzzy logic systems; universal approximators; Approximation methods; Artificial neural networks; Computer architecture; Equations; Firing; Fuzzy logic; Fuzzy neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2013 International Joint Conference on

Conference_Location

Dallas, TX

ISSN

2161-4393

Print_ISBN

978-1-4673-6128-6

Type

conf

DOI

10.1109/IJCNN.2013.6706738

Filename

6706738