Structure and learning algorithms of a nonsingleton input fuzzy neural network based on the approximate analogical reasoning schema

DOI10.1016/J.FSS.2005.12.010zbMath1101.68082OpenAlexW2090603083MaRDI QIDQ2508923

Yanyan Li

Publication date: 20 October 2006

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.fss.2005.12.010

zbMATH Keywords

similarity measuresPOPapproximate analogical reasoning schema (AARS)integrated fuzzy neural networksMackay glassmodification functionsNakanishi data setpseudo-outer-product learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Incremental learning of dynamic fuzzy neural networks for accurate system modeling

Cites Work

• Unnamed Item
• POPFNN: A pseudo outer-product based fuzzy neural network
• The concept of a linguistic variable and its application to approximate reasoning. I
• A review and comparison of six reasoning methods
• A novel approach to the derivation of fuzzy membership functions using the Falcon-MART architecture
• Outline of a New Approach to the Analysis of Complex Systems and Decision Processes

This page was built for publication: Structure and learning algorithms of a nonsingleton input fuzzy neural network based on the approximate analogical reasoning schema