Function Approximation with ARTMAP Architectures

Authors

Lucian M. Sasu, Transilvania University of Braşov
Răzvan Andonie, Central Washington University

Department or Administrative Unit

Computer Science

Document Type

Article

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Author Copyright

Copyright © 2006-2012 by CCC Publications

Publication Date

12-2012

Journal

​International Journal of Computers Communications & Control

Abstract

We analyze function approximation (regression) capability of Fuzzy ARTMAP (FAM) architectures - well-known incremental learning neural networks. We focus especially on the universal approximation property. In our experiments, we compare the regression performance of FAM networks with other standard neural models. It is the first time that ARTMAP regression is overviewed, both from theoretical and practical points of view.

Comments

This article was originally published Open Access in International Journal of Computers Communications & Control . The full-text article from the publisher can be found here.

Recommended Citation

SASU, Lucian M.; ANDONIE, Răzvan. Function Approximation with ARTMAP Architectures. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 7, n. 5, p. 957-967, sep. 2014. ISSN 1841-9844. Available at: . doi: https://doi.org/10.15837/ijccc.2012.5.1355.