Performance of a recurrent neuro-fuzzy ART based system for pattern recognition and modeling of dynamical systems: RFasArt

Author

Palmero, G. I Sainz ; Santamaria, J. Juez ; Dimitriadis, Y.A.

Author_Institution

Dept. Syst. Eng. & Control, Valladolid Univ., Spain

Volume

1

fYear

2001

fDate

25-28 July 2001

Firstpage

534

Abstract

Two ART based neuro-fuzzy systems, FasArt and its recurrent version RFasArt, are compared in order to test the performance of the recurrency in this type of architecture. Both models have been employed in several areas such as pattern recognition and modeling/identification of systems. In the first area, document understanding was involved, here the document components should be classified according to the relationships amongst them. In the second area a waste water treatment plant and an AC electrical motor have been selected. Better successful rates, non-ambiguities in the classification process and a reduction of the number of fuzzy rules or the complexity have been some of the main advantages

Keywords

AC motors; ART neural nets; document image processing; fuzzy neural nets; identification; image classification; recurrent neural nets; water treatment; AC electrical motor; FasArt; RFasArt; classification process; complexity reduction; document understanding; dynamical systems; fuzzy rules; identification; modeling; pattern recognition; recurrency; recurrent neuro-fuzzy ART based system; waste water treatment plant; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Neural networks; Neurons; Pattern recognition; Resonance; Subspace constraints; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-7078-3

Type

conf

DOI

10.1109/NAFIPS.2001.944309

Filename

944309