Improvements on the Convergence and Stability of Fuzzy Grey Cognitive Maps

Fuzzy grey cognitive maps (FGCMs) are extensions of fuzzy cognitive maps (FCMs), where the causal connections between the concepts are represented by so-called grey numbers. Just like in classical FCMs, the inference is determined by an iteration process, which may converge to an equilibrium point,...

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Autores principales: Harmati, István Á., Kóczy, László T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274685/
http://dx.doi.org/10.1007/978-3-030-50153-2_38
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author Harmati, István Á.
Kóczy, László T.
author_facet Harmati, István Á.
Kóczy, László T.
author_sort Harmati, István Á.
collection PubMed
description Fuzzy grey cognitive maps (FGCMs) are extensions of fuzzy cognitive maps (FCMs), where the causal connections between the concepts are represented by so-called grey numbers. Just like in classical FCMs, the inference is determined by an iteration process, which may converge to an equilibrium point, but limit cycles or chaotic behaviour may also show up. In this paper, based on network measures like in-degree, out-degree and connectivity, we provide new sufficient conditions for the existence and uniqueness of fixed points for FGCMs. Moreover, a tighter convergence condition is presented using the spectral radius of the modified weight matrix.
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spelling pubmed-72746852020-06-08 Improvements on the Convergence and Stability of Fuzzy Grey Cognitive Maps Harmati, István Á. Kóczy, László T. Information Processing and Management of Uncertainty in Knowledge-Based Systems Article Fuzzy grey cognitive maps (FGCMs) are extensions of fuzzy cognitive maps (FCMs), where the causal connections between the concepts are represented by so-called grey numbers. Just like in classical FCMs, the inference is determined by an iteration process, which may converge to an equilibrium point, but limit cycles or chaotic behaviour may also show up. In this paper, based on network measures like in-degree, out-degree and connectivity, we provide new sufficient conditions for the existence and uniqueness of fixed points for FGCMs. Moreover, a tighter convergence condition is presented using the spectral radius of the modified weight matrix. 2020-05-16 /pmc/articles/PMC7274685/ http://dx.doi.org/10.1007/978-3-030-50153-2_38 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Harmati, István Á.
Kóczy, László T.
Improvements on the Convergence and Stability of Fuzzy Grey Cognitive Maps
title Improvements on the Convergence and Stability of Fuzzy Grey Cognitive Maps
title_full Improvements on the Convergence and Stability of Fuzzy Grey Cognitive Maps
title_fullStr Improvements on the Convergence and Stability of Fuzzy Grey Cognitive Maps
title_full_unstemmed Improvements on the Convergence and Stability of Fuzzy Grey Cognitive Maps
title_short Improvements on the Convergence and Stability of Fuzzy Grey Cognitive Maps
title_sort improvements on the convergence and stability of fuzzy grey cognitive maps
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274685/
http://dx.doi.org/10.1007/978-3-030-50153-2_38
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