Towards the experimental evaluation of novel supervised fuzzy adaptive resonance theory for pattern classification

Authors:
Alireza Akhbardeh; Nikhil;Perttu E. Koskinen;Olli Yli-Harja
Affiliations:
Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland and School of Biomedical Engineering, Science and Health Systems, Drexel University, 3141 Che ...;Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland and Institute of Environmental Engineering and Biotechnology, Tampere University of Technolog ...;Institute of Environmental Engineering and Biotechnology, Tampere University of Technology, P.O. Box 541, 33101 Tampere, Finland;Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland
Venue:
Pattern Recognition Letters
Year:
2008

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Abstract

This paper presents a comparative analysis of novel supervised fuzzy adaptive resonance theory (SF-ART), multilayer perceptron (MLP) and competitive neural trees (CNeT) Networks over three pattern recognition problems. We have used two well-known patterns (IRIS and Vowel data) and a biological data (hydrogen data) to evaluate and check SF-ART stability, reliability, learning speed and computational load. The comparative tests with IRIS, Vowels and H"2 data indicate that the SF-ART is capable to perform with a high classification performance, high learning speed (elapsed time for learning around half second), and very low computational load compared to the well-known neural networks such as MLP and CNeT which need minutes and seconds respectively to learn the training material.