000 02771nam a22005175i 4500
001 978-3-319-28862-8
003 DE-He213
005 20200421112550.0
007 cr nn 008mamaa
008 160223s2016 gw | s |||| 0|eng d
020 _a9783319288628
_9978-3-319-28862-8
024 7 _a10.1007/978-3-319-28862-8
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aSanchez, Daniela.
_eauthor.
245 1 0 _aHierarchical Modular Granular Neural Networks with Fuzzy Aggregation
_h[electronic resource] /
_cby Daniela Sanchez, Patricia Melin.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aVIII, 101 p. 57 illus., 50 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-530X
505 0 _aIntroduction -- Background and Theory -- Proposed Method -- Application to Human Recognition -- Experimental Results -- Conclusions.
520 _aIn 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 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aNeural networks (Computer science).
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
700 1 _aMelin, Patricia.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319288611
830 0 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-530X
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-28862-8
912 _aZDB-2-ENG
942 _cEBK
999 _c58832
_d58832