Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
1-1991
Abstract
The paper introduces the basic features of fuzzy neural logic network. Each fuzzy neural logic network model is trained from a set of knowledge in the form of examples using one of the three learning algorithms introduced. These three learning algorithms are the delta rule controlled learning algorithm and two mathematical construction algorithms, namely, the local learning method and the global learning method. Once the fuzzy neural logic network model is constructed, it is ready to accept any unknown input from the user. With a low percentage of mismatched features, output solution can be obtained.
Discipline
Databases and Information Systems | OS and Networks
Research Areas
Data Science and Engineering
Publication
Proceedings of the 24th 1991 Annual Hawaii International Conference on System Sciences (HICSS): Kauai, HI, January 8-11
First Page
476
Last Page
485
Identifier
10.1109/HICSS.1991.183918
Publisher
IEEE Computer Society
City or Country
Los Alamos
Citation
NAH, Fiona Fui-hoon and NAH Fiona.
Fuzzy neural logic network and its learning algorithms. (1991). Proceedings of the 24th 1991 Annual Hawaii International Conference on System Sciences (HICSS): Kauai, HI, January 8-11. 476-485.
Available at: https://ink.library.smu.edu.sg/sis_research/10064
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/HICSS.1991.183918