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

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.