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Hierarchical modular granular neural networks with fuzzy aggregation

In this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus result...

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Detalles Bibliográficos
Autores principales: Sanchez, Daniela, Melin, Patricia
Lenguaje:eng
Publicado: Springer 2016
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-28862-8
http://cds.cern.ch/record/2137860
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author Sanchez, Daniela
Melin, Patricia
author_facet Sanchez, Daniela
Melin, Patricia
author_sort Sanchez, Daniela
collection CERN
description In this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus resulting in a new concept of MNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL) and hierarchical genetic algorithms (HGAs) are techniques used in this research work to improve results. These techniques are chosen because in other works have demonstrated to be a good option, and in the case of MNNs and HGAs, these techniques allow to improve the results obtained than with their conventional versions; respectively artificial neural networks and genetic algorithms.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2016
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spelling cern-21378602021-04-21T19:46:01Zdoi:10.1007/978-3-319-28862-8http://cds.cern.ch/record/2137860engSanchez, DanielaMelin, PatriciaHierarchical modular granular neural networks with fuzzy aggregationEngineeringIn this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus resulting in a new concept of MNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL) and hierarchical genetic algorithms (HGAs) are techniques used in this research work to improve results. These techniques are chosen because in other works have demonstrated to be a good option, and in the case of MNNs and HGAs, these techniques allow to improve the results obtained than with their conventional versions; respectively artificial neural networks and genetic algorithms.Springeroai:cds.cern.ch:21378602016
spellingShingle Engineering
Sanchez, Daniela
Melin, Patricia
Hierarchical modular granular neural networks with fuzzy aggregation
title Hierarchical modular granular neural networks with fuzzy aggregation
title_full Hierarchical modular granular neural networks with fuzzy aggregation
title_fullStr Hierarchical modular granular neural networks with fuzzy aggregation
title_full_unstemmed Hierarchical modular granular neural networks with fuzzy aggregation
title_short Hierarchical modular granular neural networks with fuzzy aggregation
title_sort hierarchical modular granular neural networks with fuzzy aggregation
topic Engineering
url https://dx.doi.org/10.1007/978-3-319-28862-8
http://cds.cern.ch/record/2137860
work_keys_str_mv AT sanchezdaniela hierarchicalmodulargranularneuralnetworkswithfuzzyaggregation
AT melinpatricia hierarchicalmodulargranularneuralnetworkswithfuzzyaggregation