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Uncertainty propagation in fuzzy grey cognitive maps with hebbian-like learning algorithms

dc.contributor.authorSalmeron J.L.
dc.contributor.authorPalos-Sanchez P.R.
dc.date.accessioned2020-09-02T22:27:42Z
dc.date.available2020-09-02T22:27:42Z
dc.date.issued2019
dc.identifier10.1109/TCYB.2017.2771387
dc.identifier.citation49, 1, 211-220
dc.identifier.issn21682267
dc.identifier.urihttps://hdl.handle.net/20.500.12728/6167
dc.descriptionThis paper is focused on an innovative fuzzy cognitive maps extension called fuzzy grey cognitive maps (FGCMs). FGCMs are a mixture of fuzzy cognitive maps and grey systems theory. These have become a useful framework for facing problems with high uncertainty, under discrete small and incomplete datasets. This paper deals with the problem of uncertainty propagation in FGCM dynamics with Hebbian learning. In addition, this paper applies differential Hebbian learning (DHL) and balanced DHL to FGCMs for the first time. We analyze the uncertainty propagation in eight different scenarios in a classical chemical control problem. The results give insight into the propagation of the uncertainty or greyness in the iterations of the FGCMs. The results show that the nonlinear Hebbian learning is the choice with less uncertainty in steady final grey states for Hebbian learning algorithms. © 2013 IEEE.
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectFuzzy grey cognitive map (FGCM)
dc.subjectgrey systems
dc.subjectHebbian learning
dc.subjectuncertainty propagation
dc.subjectunsuper-vised learning
dc.subjectChemical analysis
dc.subjectCognitive systems
dc.subjectFuzzy rules
dc.subjectSystem theory
dc.subjectUncertainty analysis
dc.subjectUnsupervised learning
dc.subjectChemical controls
dc.subjectCognitive maps
dc.subjectFuzzy cognitive map
dc.subjectFuzzy grey cognitive maps
dc.subjectGrey systems
dc.subjectHebbian learning
dc.subjectHebbian learning algorithm
dc.subjectUncertainty propagation
dc.subjectLearning algorithms
dc.titleUncertainty propagation in fuzzy grey cognitive maps with hebbian-like learning algorithms
dc.typeArticle


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