Title

Classification of noisy signals using fuzzy ARTMAP neural networks

Authors

Authors

D. Charalampidis; T. Kasparis;M. Georgiopoulos

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

IEEE Trans. Neural Netw.

Keywords

classification; energy; fractal dimension; fuzzy ARTMAP; noise; segmentation; texture; UNSUPERVISED TEXTURE SEGMENTATION; GABOR FILTERS; MULTIDIMENSIONAL MAPS; ARCHITECTURE; Computer Science, Artificial Intelligence; Computer Science, Hardware &; Architecture; Computer Science, Theory & Methods; Engineering, ; Electrical & Electronic

Abstract

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 grayscale 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.

Journal Title

Ieee Transactions on Neural Networks

Volume

12

Issue/Number

5

Publication Date

1-1-2001

Document Type

Article

Language

English

First Page

1023

Last Page

1036

WOS Identifier

WOS:000171123100006

ISSN

1045-9227

Share

COinS