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Proceedings Paper

Experimental comparison of FOSART and FLVQ in a remotely sensed image classification task
Author(s): Palma N. Blonda; Andrea Baraldi; G. Bafunno; Giuseppe Satalino; G. Ria
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Paper Abstract

This paper deals with the application of a new competitive, on-line, neuro-fuzzy architecture, the fully self-organizing simplified adaptive resonance theory (FOSART), to the analysis of remote sensed Antarctic data, in a classification experiment. FOSART employs fuzzy set memberships in the weights updating rule; it applies an ART-based vigilance test to control neuron proliferation and takes advantage of the fact that it employs a new version of the competitive Hebbian Rule to dynamically generate and remove synaptic links between neurons, as well as neurons. As a consequence, FOSART can develop disjointed subnets. The results obtained with FOSART have been compared with those obtained with other neuro-fuzzy unsupervised architecture: FuzzySART, FLVQ, SOM. The finding suggests that FOSART performances are lower, at convergence, than those of FLVQ and SOM, even if it shows a faster adaptivity to the input data structure, due to its topological and on-line characteristics.

Paper Details

Date Published: 13 October 1997
PDF: 10 pages
Proc. SPIE 3165, Applications of Soft Computing, (13 October 1997); doi: 10.1117/12.290270
Show Author Affiliations
Palma N. Blonda, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)
Andrea Baraldi, Istituto per lo studio delle Metodologie Geofisiche Ambientali/CNR (Italy)
G. Bafunno, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)
Giuseppe Satalino, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)
G. Ria, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)


Published in SPIE Proceedings Vol. 3165:
Applications of Soft Computing
Bruno Bosacchi; James C. Bezdek; David B. Fogel, Editor(s)

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