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Neuro-fuzzy system for large data sets

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

The paper describes the neuro-fuzzy system for large data sets. The large data set is split into subsets and independent submodels are elaborated. The models are then merged. The described approach enables realisation of incremental learning paradigm. The paper proposes new measure of rule quality based on the logical implications and measure for similarity of rules in neuro-fuzzy systems. The theory is accompanied by experimental results.

Original languageEnglish
Title of host publicationMan-Machine Interactions 2
EditorsTadeusz Czachorski, Stanislaw Kozielski, Urszula Stanczyk
Pages297-304
Number of pages8
DOIs
Publication statusPublished - 2011

Publication series

NameAdvances in Intelligent and Soft Computing
Volume103
ISSN (Print)1867-5662

Keywords

  • large data sets
  • neuro-fuzzy systems
  • rule quality
  • rules similarity

ASJC Scopus subject areas

  • General Computer Science

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