Fuzzy relations neural network: some preliminary results

Author

Ciaramella, A. ; Tagliaferri, R. ; Pedrycz, Witold

Author_Institution

Dept. of Math. & Comput. Sci., Salerno Univ., Italy

Volume

1

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

469

Lastpage

472

Abstract

In this paper, a neuro-fuzzy model is introduced. The model describes a fuzzy relational "IF-THEN" reasoning scheme using an adaptive structure based on fuzzy relations. Two training schemes for the learning of the parameters based respectively on the backpropagation algorithm and pseudo-inverse matrix technique are illustrated. The model qualities are investigated by a series of simulation examples: function approximation, classification and rule extraction. These preliminary and promising results show that the model has a good performance and that it could be used for complex systems in real world applications

Keywords

adaptive systems; backpropagation; function approximation; fuzzy neural nets; inference mechanisms; pattern classification; adaptive structure; backpropagation; function approximation; fuzzy neural network; fuzzy relational reasoning; learning; neural fuzzy model; pattern classification; pseudoinverse matrix; rule extraction; training schemes; Biological neural networks; Bismuth; Fuzzy control; Fuzzy neural networks; Fuzzy reasoning; Fuzzy set theory; Fuzzy systems; Mathematical model; Neural networks; Power system modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2001. The 10th IEEE International Conference on

Conference_Location

Melbourne, Vic.

Print_ISBN

0-7803-7293-X

Type

conf

DOI

10.1109/FUZZ.2001.1007350

Filename

1007350