Training Fuzzy Cognitive Maps Using Gradient-Based Supervised Learning - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2013

Training Fuzzy Cognitive Maps Using Gradient-Based Supervised Learning

Michal Gregor
  • Fonction : Auteur
  • PersonId : 1000652
Peter P. Groumpos
  • Fonction : Auteur
  • PersonId : 1000591

Résumé

The paper considers a novel approach to learning the weight matrix of a fuzzy cognitive map. An overview of the state-of-the-art learning methods is presented with a specific emphasis on methods initially developed for artificial neural networks, and later adapted for FCMs. These have mostly been based on the concept of Hebbian learning. Inspired by the amount of success these methods have faced in the past, the paper proposes a new approach based on the application of the delta rule and the principle of backpropagation, both of which were originally designed for artificial neural networks as well. It is shown by simulation experiments and comparison with the existing approach based on nonlinear Hebbian learning that the proposed approach achieves favourable results, and that these are superior to those of the existing method by several orders of magnitude. Finally, some possible lines of further investigation are suggested.
Fichier principal
Vignette du fichier
978-3-642-41142-7_55_Chapter.pdf (225.82 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01459646 , version 1 (07-02-2017)

Licence

Paternité

Identifiants

Citer

Michal Gregor, Peter P. Groumpos. Training Fuzzy Cognitive Maps Using Gradient-Based Supervised Learning. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. pp.547-556, ⟨10.1007/978-3-642-41142-7_55⟩. ⟨hal-01459646⟩
83 Consultations
214 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More