A neuro-fuzzy approach to obtain interpretable fuzzy systems for function approximation

Author

Nauck, Detlef ; Kruse, Rudolf

Author_Institution

Fac. of Comput. Sci., Univ. of Magdeburg, Germany

Volume

2

fYear

1998

fDate

4-9 May 1998

Firstpage

1106

Abstract

Fuzzy systems can be used for function approximation based on a set of linguistic rules. We present a method to obtain the necessary parameters for such a fuzzy system by a neuro-fuzzy training method. The learning algorithm is able to determine the structure and the parameters of a fuzzy system from sample data. The approach is an extension to our already published NEFCON and NEFCLASS models which are used for control or classification purposes. The NEFPROX model, which is discussed in this paper is more general, and it can be used for any problem based on function approximation. We especially consider the problem to obtain interpretable fuzzy systems by learning

Keywords

feedforward neural nets; function approximation; fuzzy neural nets; fuzzy systems; learning (artificial intelligence); multilayer perceptrons; NEFCLASS models; NEFCON models; NEFPROX model; function approximation; interpretable fuzzy systems; learning algorithm; linguistic rules; neuro-fuzzy approach; neuro-fuzzy training method; Computer science; Error correction; Function approximation; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Neural networks; Supervised learning; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7584

Print_ISBN

0-7803-4863-X

Type

conf

DOI

10.1109/FUZZY.1998.686273

Filename

686273