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Fuzzy cognitive maps for applied sciences and engineering: from fundamentals to extensions and learning algorithms

Fuzzy Cognitive Maps (FCM) constitute cognitive models in the form of fuzzy directed graphs consisting of two basic elements: the nodes, which basically correspond to “concepts” bearing different states of activation depending on the knowledge they represent, and the “edges” denoting the causal effe...

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Detalles Bibliográficos
Autor principal: Papageorgiou, Elpiniki
Lenguaje:eng
Publicado: Springer 2014
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-642-39739-4
http://cds.cern.ch/record/1642358
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author Papageorgiou, Elpiniki
author_facet Papageorgiou, Elpiniki
author_sort Papageorgiou, Elpiniki
collection CERN
description Fuzzy Cognitive Maps (FCM) constitute cognitive models in the form of fuzzy directed graphs consisting of two basic elements: the nodes, which basically correspond to “concepts” bearing different states of activation depending on the knowledge they represent, and the “edges” denoting the causal effects that each source node exercises on the receiving concept expressed through weights. Weights take values in the interval [-1,1], which denotes the positive, negative or neutral causal relationship between two concepts. An FCM can be typically obtained through linguistic terms, inherent to fuzzy systems, but with a structure similar to the neural networks, which facilitates data processing, and has capabilities for training and adaptation. During the last 10 years, an exponential growth of published papers in FCMs was followed showing great impact potential. Different FCM structures and learning schemes have been developed, while numerous studies report their use in many contexts with highly successful modeling results.   The aim of this book is to fill the existing gap in the literature concerning fundamentals, models, extensions and learning algorithms for FCMs in knowledge engineering. It comprehensively covers the state-of-the-art FCM modeling and learning methods, with algorithms, codes and software tools, and provides a set of applications that demonstrate their various usages in applied sciences and engineering.
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spelling cern-16423582021-04-21T21:22:13Zdoi:10.1007/978-3-642-39739-4http://cds.cern.ch/record/1642358engPapageorgiou, ElpinikiFuzzy cognitive maps for applied sciences and engineering: from fundamentals to extensions and learning algorithmsEngineeringFuzzy Cognitive Maps (FCM) constitute cognitive models in the form of fuzzy directed graphs consisting of two basic elements: the nodes, which basically correspond to “concepts” bearing different states of activation depending on the knowledge they represent, and the “edges” denoting the causal effects that each source node exercises on the receiving concept expressed through weights. Weights take values in the interval [-1,1], which denotes the positive, negative or neutral causal relationship between two concepts. An FCM can be typically obtained through linguistic terms, inherent to fuzzy systems, but with a structure similar to the neural networks, which facilitates data processing, and has capabilities for training and adaptation. During the last 10 years, an exponential growth of published papers in FCMs was followed showing great impact potential. Different FCM structures and learning schemes have been developed, while numerous studies report their use in many contexts with highly successful modeling results.   The aim of this book is to fill the existing gap in the literature concerning fundamentals, models, extensions and learning algorithms for FCMs in knowledge engineering. It comprehensively covers the state-of-the-art FCM modeling and learning methods, with algorithms, codes and software tools, and provides a set of applications that demonstrate their various usages in applied sciences and engineering.Springeroai:cds.cern.ch:16423582014
spellingShingle Engineering
Papageorgiou, Elpiniki
Fuzzy cognitive maps for applied sciences and engineering: from fundamentals to extensions and learning algorithms
title Fuzzy cognitive maps for applied sciences and engineering: from fundamentals to extensions and learning algorithms
title_full Fuzzy cognitive maps for applied sciences and engineering: from fundamentals to extensions and learning algorithms
title_fullStr Fuzzy cognitive maps for applied sciences and engineering: from fundamentals to extensions and learning algorithms
title_full_unstemmed Fuzzy cognitive maps for applied sciences and engineering: from fundamentals to extensions and learning algorithms
title_short Fuzzy cognitive maps for applied sciences and engineering: from fundamentals to extensions and learning algorithms
title_sort fuzzy cognitive maps for applied sciences and engineering: from fundamentals to extensions and learning algorithms
topic Engineering
url https://dx.doi.org/10.1007/978-3-642-39739-4
http://cds.cern.ch/record/1642358
work_keys_str_mv AT papageorgiouelpiniki fuzzycognitivemapsforappliedsciencesandengineeringfromfundamentalstoextensionsandlearningalgorithms