The learning of neuro-fuzzy approximator with fuzzy rough sets in case of missing features

Author

Nowicki, Robert K. ; Nowak, Bartosz A. ; Starczewski, Janusz T. ; Cpalka, Krzysztof

Author_Institution

Inst. of Comput. Intell., Czestochowa Univ. of Technol., Czestochowa, Poland

fYear

2014

fDate

6-11 July 2014

Firstpage

3759

Lastpage

3766

Abstract

The architecture of neuro-fuzzy systems with fuzzy rough sets originally has been developed to process with imprecise data. In this paper, the adaptation of those systems to the missing features case is presented. However, the main considerations concern with methods of learning which could be applied to such systems for approximation tasks. Various methods for determining values of system parameters have been considered, in particular the gradient learning method. The effectiveness of proposed methods has been confirmed by many simulation experiments, which results have been supplied to this paper.

Keywords

approximation theory; fuzzy neural nets; learning (artificial intelligence); rough set theory; approximation system; fuzzy rough sets; gradient learning method; missing features; neuro-fuzzy approximator learning; neurofuzzy system architecture; Approximation methods; Fuzzy sets; Learning systems; Neural networks; Rough sets; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), 2014 International Joint Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4799-6627-1

Type

conf

DOI

10.1109/IJCNN.2014.6889857

Filename

6889857