Modelling radial basis functions with rational logic rules

Davide Sottara, Paola Mello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Connectionist systems such as Radial Basis Function Neural Networks and similar architectures are commonly applied to solve problems of learning relations from available examples. To overcome their limits in clarity of representation, they are often interfaced with symbolic rule-based systems, provided that the information they have memorized can be interpreted. In this paper, an automatic implementation of a RBF-like system is presented using only gradual fuzzy rules learned by induction directly from training data. It is then shown that the same formalism, used with type-II truth values, can learn second-order, fuzzy relations.

Original languageEnglish (US)
Title of host publicationHybrid Artificial Intelligence Systems - Third International Workshop, HAIS 2008, Proceedings
Pages337-344
Number of pages8
DOIs
StatePublished - Dec 1 2008
Event3rd International Workshop on Hybrid Artificial Intelligence Systems, HAIS 2008 - Burgos, Spain
Duration: Sep 24 2008Sep 26 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5271 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Workshop on Hybrid Artificial Intelligence Systems, HAIS 2008
CountrySpain
CityBurgos
Period9/24/089/26/08

Keywords

  • Fuzzy logic
  • Induction
  • Radial basis function

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Sottara, D., & Mello, P. (2008). Modelling radial basis functions with rational logic rules. In Hybrid Artificial Intelligence Systems - Third International Workshop, HAIS 2008, Proceedings (pp. 337-344). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5271 LNAI). https://doi.org/10.1007/978-3-540-87656-4_42