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Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation: SpringerBriefs in Applied Sciences and Technology

Autor Daniela Sanchez, Patricia Melin
en Limba Engleză Paperback – 2 mar 2016
In this book, anew method for hybrid intelligent systems is proposed. The proposed method isbased on a granular computing approach applied in two levels. The techniquesused and combined in the proposed method are modular neural networks (MNNs)with a Granular Computing (GrC) approach, thus resulting in a new concept ofMNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL)and hierarchical genetic algorithms (HGAs) are techniques used in this researchwork to improve results. These techniques are chosen because in other workshave demonstrated to be a good option, and in the case of MNNs and HGAs, thesetechniques allow to improve the results obtained than with their conventionalversions; respectively artificial neural networks and genetic algorithms.
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Specificații

ISBN-13: 9783319288611
ISBN-10: 331928861X
Pagini: 110
Ilustrații: VIII, 101 p. 57 illus., 50 illus. in color.
Dimensiuni: 155 x 235 x 6 mm
Greutate: 0.17 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Seriile SpringerBriefs in Applied Sciences and Technology, SpringerBriefs in Computational Intelligence

Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

Introduction.- Backgroundand Theory.- Proposed Method.- Applicationto Human Recognition.- ExperimentalResults.- Conclusions.

Textul de pe ultima copertă

In this book, anew method for hybrid intelligent systems is proposed. The proposed method isbased on a granular computing approach applied in two levels. The techniquesused and combined in the proposed method are modular neural networks (MNNs)with a Granular Computing (GrC) approach, thus resulting in a new concept ofMNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL)and hierarchical genetic algorithms (HGAs) are techniques used in this researchwork to improve results. These techniques are chosen because in other workshave demonstrated to be a good option, and in the case of MNNs and HGAs, thesetechniques allow to improve the results obtained than with their conventionalversions; respectively artificial neural networks and genetic algorithms.

Caracteristici

Introduces a new model of a modular neural network based on a granular approach Serves as reference book for scientists and engineers interested in applying soft computing Presents recent research Includes supplementary material: sn.pub/extras