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...

Full description

Main Authors: Sanchez, Daniela. (Author, http://id.loc.gov/vocabulary/relators/aut), Melin, Patricia. (http://id.loc.gov/vocabulary/relators/aut)
Corporate Author: SpringerLink (Online service)
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edition:1st ed. 2016.
Series:SpringerBriefs in Computational Intelligence,
Subjects:
Online Access:https://doi.org/10.1007/978-3-319-28862-8
LEADER 03130nam a22005175i 4500
001 978-3-319-28862-8
003 DE-He213
005 20210619114505.0
007 cr nn 008mamaa
008 160223s2016 gw | s |||| 0|eng d
020 |a 9783319288628  |9 978-3-319-28862-8 
024 7 |a 10.1007/978-3-319-28862-8  |2 doi 
050 4 |a Q342 
072 7 |a UYQ  |2 bicssc 
072 7 |a TEC009000  |2 bisacsh 
072 7 |a UYQ  |2 thema 
082 0 4 |a 006.3  |2 23 
100 1 |a Sanchez, Daniela.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
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 
347 |a text file  |b PDF  |2 rda 
490 1 |a SpringerBriefs in Computational Intelligence,  |x 2625-3704 
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 Computational intelligence. 
650 0 |a Artificial intelligence. 
650 0 |a Neural networks (Computer science) . 
650 1 4 |a Computational Intelligence.  |0 https://scigraph.springernature.com/ontologies/product-market-codes/T11014 
650 2 4 |a Artificial Intelligence.  |0 https://scigraph.springernature.com/ontologies/product-market-codes/I21000 
650 2 4 |a Mathematical Models of Cognitive Processes and Neural Networks.  |0 https://scigraph.springernature.com/ontologies/product-market-codes/M13100 
700 1 |a Melin, Patricia.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783319288611 
776 0 8 |i Printed edition:  |z 9783319288635 
830 0 |a SpringerBriefs in Computational Intelligence,  |x 2625-3704 
856 4 0 |u https://doi.org/10.1007/978-3-319-28862-8 
912 |a ZDB-2-ENG 
912 |a ZDB-2-SXE 
950 |a Engineering (SpringerNature-11647) 
950 |a Engineering (R0) (SpringerNature-43712)