Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems

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

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)


A detailed comparative analysis of the Hebbian-like learning algorithms applied to train Fuzzy Cognitive Maps (FCMs) operating as pattern classifiers, is presented in this paper. These algorithms aim to find appropriate weights between the concepts of the FCM classifier so it equilibrates to a desired state (class mapping). For these purposes, six different types of Hebbian learning algorithms from the literature have been selected and studied in this work. Along with the theoretical description of these algorithms and the analysis of their performance in classifying known patterns, a sensitivity analysis of the applied classification scheme, regarding some configuration parameters have taken place. It is worth noting that the algorithms are studied in a comparative fashion, under common configurations for several benchmark pattern classification datasets, by resulting to useful conclusions about their training capabilities.

Original languageEnglish
Pages (from-to)10620-10629
Number of pages10
JournalExpert Systems with Applications
Issue number12
Publication statusPublished - Sep 15 2012


  • Classifier
  • Fuzzy Cognitive Maps
  • Hebbian learning
  • Pattern classification
  • Soft computing
  • Training

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems'. Together they form a unique fingerprint.

Cite this