A Modified Fuzzy ARTMAP Architecture for Incremental Learning Function Approximation

Rǎzvan Andonie, Lucian Sasu, Valeriu Beiu

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

7 Citations (Scopus)

Abstract

We will focus here on approximating functions that map from the vector-valued real domain to the vector-valued real range. A Fuzzy ARTMAP (FAM) architecture, called Fuzzy Artmap with Relevance factor (FAMR, defined in [1]) is considered here as an alternative to function approximation. FAMR uses a relevance factor assigned to each sample pair, proportional to the importance of the respective pair during the learning phase, and is a generalization of PROBART (a FAM architecture defined in). Like other FAM-based systems, FAMR can be incrementally trained.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence
PublisherInt. Assoc. of Science and Technology for Development
Pages124-129
Number of pages6
ISBN (Print)0889863474, 9780889863477
Publication statusPublished - 2003
Externally publishedYes
EventProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence - Cancun, Mexico
Duration: May 19 2003May 21 2003

Publication series

NameProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence

Other

OtherProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence
Country/TerritoryMexico
CityCancun
Period5/19/035/21/03

Keywords

  • Function approximation
  • Fuzzy ARTMAP
  • Incremental learning

ASJC Scopus subject areas

  • General Engineering

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