Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7156
Title: Classification of noisy signals using fuzzy ARTMAP neural networks
Authors: Kasparis, Takis 
Georgiopoulos, Michael N.
Charalampidis, Dimitrios
Keywords: Neural networks;Fractals;Fuzzy sets;Image analysis
Issue Date: 2000
Publisher: IEEE
Source: International Joint Conference on Neural Networks, 2000, Como, Italy
Abstract: This paper describes an approach to classification of noisy signals using a technique based on the Fuzzy ARTMAP neural network (FAM). A variation of the testing phase of Fuzzy ARTMAP is introduced, that exhibited superior generalization performance than the standard Fuzzy ARTMAP in the presence of noise. We present an application of our technique for textured grayscale images. We perform a large number of experiments to verify the superiority of the modified over the standard Fuzzy ARTMAP. More specifically, the modified and the standard FAM were evaluated on two different sets of features (fractal-based and energy-based), for three different types of noise (Gaussian, uniform, exponential) and for two different texture sets (Brodatz, aerial). Furthermore, the classification performance of the standard and modified Fuzzy ARTMAP was compared for different network sizes.
URI: http://ktisis.cut.ac.cy/handle/10488/7156
http://ktisis.cut.ac.cy/handle/10488/7156
http://hdl.handle.net/10488/7156
ISSN: 10987576
DOI: 10.1109/IJCNN.2000.859372
Rights: © 2000 IEEE
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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