Modified Fuzzy ARTMAP and Supervised Fuzzy ART: Comparative Study with Multispectral Classification

In this article a modification of the algorithm of the fuzzy ART network, aiming at returning it supervised is carried out. It consists of the search for the comparison, training and vigilance parameters giving the minimum quadratic distances between the output of the training base and those obtained by the network. The same process is applied for the determination of the parameters of the fuzzy ARTMAP giving the most powerful network. The modification consist in making learn the fuzzy ARTMAP a base of examples not only once as it is of use, but as many time as its architecture is in evolution or than the objective error is not reached . In this way, we don-t worry about the values to impose on the eight (08) parameters of the network. To evaluate each one of these three networks modified, a comparison of their performances is carried out. As application we carried out a classification of the image of Algiers-s bay taken by SPOT XS. We use as criterion of evaluation the training duration, the mean square error (MSE) in step control and the rate of good classification per class. The results of this study presented as curves, tables and images show that modified fuzzy ARTMAP presents the best compromise quality/computing time.

• F.Alilat
• S.Loumi
• H.Merrad
• B.Sansal

• Neural Networks
• fuzzy ART
• fuzzy ARTMAP
• Remote sensing
• multispectral Classification.

[1] A. H Tan, "Cascade ARTMAP Integrating Neural Computation and
Symbolic Knowledge Processing." IEEE Transactions on Neural
Networks, Vol.8 No2, 1997.
[2] I. Dagher, M. Georgiopoulos, G. L. Heileman, G. Bebis,," An Ordering
Algorithm for Pattern Presentation in Fuzzy ARTMAP That Tends to
Improve Generalization Performance", IEEE Transactions on Neural
Networks, Vol. 10 No. 4,1999
[3] D. Charalampidis, T. Kasparis, M. Georgiopoulos, "Classification of
Noisy Signals Using Fuzzy ARTMAP Neural Networks", IEEE
Transactions on Neural Networks, Vol. 12, No. 5, 2001
[4] R. K. Aggarwal, Q. Y. Xuan, A. T. Johns, A Bennett, "A Novel
Approach to Fault Diagnosis in Multicircuit Transmission Lines Using
Fuzzy ARTmap Neural Networks", IEEE Transactions on Neural
Networks, Vol. 10, No. 5, 1999.
[5] G. A Carpenter, S. Grossberg, D. B. Rosen, "Fuzzy ART : An Adaptive
Resonance Algorithm for Rapid, stable classification of Analog
Patterns", Proc. International Joint Conference of Neural Networks,
IJCNN-91, Seattle, IEEE/INNS Inc, pp. II-411-416,1991.
[6] G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, D. Rosen,
"Fuzzy ARTMAP: A Neural Network Architecture for Incremental
Supervised Learning of Analog Multidimensional Maps", IEEE
Transactions on Neural Networks, Vol. 3 No. 5, 1992.
[7] G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, "A
Fuzzy ARTMAP Nonparametric Probability Estimator for
Nonstationary Pattern Recognition Problems", IEEE Transactions on
Neural Networks, Vol. 6 No. 6, 1995.
[8] E. Gómez-Sánchez, Y. A. Dimitriadis, J. M. Cano-Izquierdo, J. López-
Coronado, "╬╝ARTMAP: Use of Mutual Information for Category
Reduction in Fuzzy ARTMAP", IEEE Transactions on Neural
Networks, Vol. 13, No1, 2002.
[9] T. Frank, K. F. Kraiss, T. Kuhlen, "Comparative Analysis of Fuzzy
ART and ART-2A Network Clustering Performance", IEEE
Transactions on Neural Networks, Vol. 9, No. 3, 1998.
[10] P. Baldi, "Gradient Descent Learning Algorithm Overview: A General
Dynamical Systems Perspective", IEEE Transactions on Neural
Networks, Vol. 6 No. 1, 1995.
[11] D. Charalampidis, G. C. Anagnostopoulos, M. Georgipoulos, T.
Kasparis, "Fuzzy ART and Fuzzy ARTMAP with adaptively weighted
distances", Proc. SPIE, Vol 4739 pp 86-97, 2002.
[12] F. Alilat, S. Loumi, H. Merrad , B. Sansal, "Nouvelle approche du
réseau ARTMAP Flou Application ├á la classification multispectrale
des images SPOT XS de la baie d-Alger", Revue Française de
Photogramétrie et de Télédétection SFPT, (2005-1),.no.177, pp 17-24,
2005.