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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Sanchez, Daniela (Συγγραφέας), Melin, Patricia (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2016.
Έκδοση:1st ed. 2016.
Σειρά:SpringerBriefs in Applied Sciences and Technology,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 02770nam a22005055i 4500
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100 1 |a Sanchez, Daniela.  |e author. 
245 1 0 |a Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation  |h [electronic resource] /  |c by Daniela Sanchez, Patricia Melin. 
250 |a 1st ed. 2016. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2016. 
300 |a VIII, 101 p. 57 illus., 50 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
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490 1 |a SpringerBriefs in Applied Sciences and Technology,  |x 2191-530X 
505 0 |a Introduction -- Background and Theory -- Proposed Method -- Application to Human Recognition -- Experimental Results -- Conclusions. 
520 |a 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. 
650 0 |a Engineering. 
650 0 |a Artificial intelligence. 
650 0 |a Neural networks (Computer science). 
650 0 |a Computational intelligence. 
650 1 4 |a Engineering. 
650 2 4 |a Computational Intelligence. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Mathematical Models of Cognitive Processes and Neural Networks. 
700 1 |a Melin, Patricia.  |e author. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783319288611 
830 0 |a SpringerBriefs in Applied Sciences and Technology,  |x 2191-530X 
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950 |a Engineering (Springer-11647)