Title

Classification Of Noisy Signals Using Fuzzy Artmap Neural Networks

Keywords

Classification; Energy; Fractal dimension; Fuzzy ARTMAP; Noise; Segmentation; Texture

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.

Publication Date

9-1-2001

Publication Title

IEEE Transactions on Neural Networks

Volume

12

Issue

5

Number of Pages

1023-1036

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/72.950132

Socpus ID

0035439760 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0035439760

This document is currently not available here.

Share

COinS