Fuzzy neural logic network and its learning algorithms

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

4 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of the Twenty-Fourth Annual Hawaii International Conference on System Sciences
PublisherIEEE
Pages476-485
Volume1
ISBN (Print)9780818621215
Publication statusPublished - Jan 1991
Externally publishedYes

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605
ISSN (Electronic)2572-6862

Conference

Title24th Annual Hawaii International Conference on System Sciences (HICSS 1991)
PlaceUnited States
CityKauai
Period8 - 11 January 1991

Abstract

This 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.

Citation Format(s)

Fuzzy neural logic network and its learning algorithms. / Chan, Sing-Chai; Nah, Fui-Hoon.

Proceedings of the Twenty-Fourth Annual Hawaii International Conference on System Sciences. Vol. 1 IEEE, 1991. p. 476-485 (Proceedings of the Annual Hawaii International Conference on System Sciences).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review