A neuro-fuzzy approach to obtain interpretable fuzzy systems for function approximation
- 27 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2 (10987584) , 1106-1111
- https://doi.org/10.1109/fuzzy.1998.686273
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
This publication has 5 references indexed in Scilit:
- Fuzzy systems as universal approximatorsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- NEFCON-I: an X-Window based simulator for neural fuzzy controllersPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A neuro-fuzzy method to learn fuzzy classification rules from dataFuzzy Sets and Systems, 1997
- Sugeno type controllers are universal controllersFuzzy Sets and Systems, 1993
- ANFIS: adaptive-network-based fuzzy inference systemIEEE Transactions on Systems, Man, and Cybernetics, 1993