Training Fuzzy Cognitive Maps by using Hebbian learning algorithms: A comparative study

G. A. Papakostas, A. S. Polydoros, D. E. Koulouriotis, V. D. Tourassis

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

14 Citations (Scopus)

Abstract

A detailed analysis of the Hebbian-like learning algorithms applied to train Fuzzy Cognitive Maps (FCMs) is presented in this paper. These algorithms aim to find appropriate weights between the concepts of the FCM so the model equilibrates to a desired state. For this manner, four different types of Hebbian learning algorithms have been proposed in the past. Along with the theoretical description of these algorithms, their performance in system modeling problems is investigated in this work. The algorithms are studied in a comparative fashion by using appropriate performance indices and useful conclusions about their training capabilities are experimentally derived.

Original languageEnglish
Title of host publicationFUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings
Pages851-858
Number of pages8
DOIs
Publication statusPublished - Sep 27 2011
Event2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei, Taiwan
Duration: Jun 27 2011Jun 30 2011

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Other

Other2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011
CountryTaiwan
CityTaipei
Period6/27/116/30/11

Keywords

  • fuzzy cognitive maps
  • hebbian learning
  • system modeling
  • training

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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    Papakostas, G. A., Polydoros, A. S., Koulouriotis, D. E., & Tourassis, V. D. (2011). Training Fuzzy Cognitive Maps by using Hebbian learning algorithms: A comparative study. In FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings (pp. 851-858). [6007544] (IEEE International Conference on Fuzzy Systems). https://doi.org/10.1109/FUZZY.2011.6007544