Classification of noisy signals using fuzzy ARTMAP neural networks

This paper describes an approach to classification of noisy signals using a technique based on the fuzzy ARTMAP neural network (FAMNN). The proposed method is a modification of the testing phase of the fuzzy ARTMAP that exhibits superior generalization performance compared to the generalization perf...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2001-09, Vol.12 (5), p.1023-1036
Main Authors: Chralampidis, D., Kasparis, T., Georgiopoulos, M.
Format: Article
Language:English
Subjects:
Classification
Fractals
Fuzzy
Fuzzy logic
Fuzzy neural networks
Fuzzy set theory
Gray-scale
Image segmentation
Nearest neighbor searches
Neural networks
Phase noise
Resonance
Subspace constraints
Surface layer
Testing
Texture
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Summary:This paper describes an approach to classification of noisy signals using a technique based on the fuzzy ARTMAP neural network (FAMNN). The proposed method is a modification of the testing phase of the fuzzy ARTMAP that exhibits superior generalization performance compared to the generalization performance of the standard fuzzy ARTMAP in the presence of noise. An application to textured gray-scale image segmentation is presented. The superiority of the proposed modification over the standard fuzzy ARTMAP is established by a number of experiments using various texture sets, feature vectors and noise types. The texture sets include various aerial photos and also samples obtained from the Brodatz album. Furthermore, the classification performance of the standard and the modified fuzzy ARTMAP is compared for different network sizes. Classification results that illustrate the performance of the modified algorithm and the FAMNN are presented.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/72.950132