Fuzzy neural logic network and its learning algorithms

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Article number183918
Pages (from-to)476-485
Journal / PublicationProceedings of the Annual Hawaii International Conference on System Sciences
Volume1
Publication statusPublished - 1991
Externally publishedYes

Conference

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

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