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Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation / by Daniela Sanchez, Patricia Melin

By: Sanchez, Daniela.
Contributor(s): SpringerLink (Online service) | Melin, Patricia (1962-)
Material type: materialTypeLabelE-bookSeries: (SpringerBriefs in Applied Sciences and Technology, 2191-530X).Publisher: Cham : Springer International Publishing, 2016Edition: 1st ed.Description: 1 recurso en línea (VIII, 101 páginas) : 57 ilustraciones, 50 ilustraciones en color.ISBN: 9783319288628.Subject: Inteligencia artificial | Redes neuronales artificialesDDC classification: 006.3 Online resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Introduction -- Background and Theory -- Proposed Method -- Application to Human Recognition -- Experimental Results -- Conclusions.
Abstract: 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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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería QA76.9.S63 S263 2016 EB (Browse shelf(Opens below)) .i11591420 Acceso electrónico eBOOK .i11591420
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Introduction -- Background and Theory -- Proposed Method -- Application to Human Recognition -- Experimental Results -- Conclusions.

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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