NDLI: Classification of noisy signal using fuzzy ARTMAP neural networks

Content Provider	IEEE Xplore Digital Library
Author	Charalampidis, D. Georgiopoulos, M. Kasparis, T.
Copyright Year	2000
Description	Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Central Florida Univ., Orlando, FL, USA (Charalampidis, D.)
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.
Starting Page	53
Ending Page	58
File Size	726758
Page Count	6
File Format	PDF
ISBN	0769506194
ISSN	10987576
DOI	10.1109/IJCNN.2000.859372
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-07-27
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Neural networks Fractals Speech Computer science Testing Phase noise Gray-scale Gaussian noise Subspace constraints
Content Type	Text
Resource Type	Article

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