Title

Classification Of Noisy Signals Using Fuzzy Artmap Neural Networks

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.

Publication Date

1-1-2000

Publication Title

Proceedings of the International Joint Conference on Neural Networks

Volume

6

Number of Pages

53-58

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

0033721119 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0033721119

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