Enhancing Performance of Relational Fuzzy Neural Networks with Square BK-Products

DOI

10.2991/jcis.2006.107How to use a DOI?

Keywords

Fuzzy Logic, Pattern Classification, Neural Network

Abstract

In this paper, we extend research done in max-min fuzzy neural networks in several important ways. We replace max and min operations use in the fuzzy operations by more general t-norms and co-norms, respectively. In addition, instead of the Łukasiewicz equivalence connective used in the network of Reyes-Garcia and Bandler, we employ in our hybridization a variety of equivalence connectives. We explore the effectiveness of this network in the domain of phoneme recognition and diabetes data. We find increased classification ability in many cases, as well as great potential for further expansion of the use of fuzzy operations in the field of pattern recognition.

Copyright

© 2006, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Warren L. Davis IV
AU  - Ladislav Kohout
PY  - 2006/10
DA  - 2006/10
TI  - Enhancing Performance of Relational Fuzzy Neural Networks with Square BK-Products
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.107
DO  - 10.2991/jcis.2006.107
ID  - DavisIV2006/10
ER  -