LEADER 00923nam0-2200313---450- 001 990008275460403321 005 20110418110133.0 010 $a3-925867-75-9 035 $a000827546 035 $aFED01000827546 035 $a(Aleph)000827546FED01 035 $a000827546 100 $a20060216d2004----km-y0itay50------ba 101 0 $aspa 102 $aDE 105 $a----a---001yy 200 1 $aBaltasar Gracián$eantropologia y estética$eactas del II coloquio internacional, Berlin, 4-7 de octubre de 2001$fed. Sebastian Neumeister 210 $aBerlin$cEdition Tranvía$d2004 215 $a336 p.$d21 cm 610 0 $aGracián, Baltasar$aCongressi$a2001 676 $a868.3 702 1$aNeumeister,$bSebastian 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990008275460403321 952 $a868.3 GRAC/S 2$fFLFBC 959 $aFLFBC 996 $aBaltasar Gracián$9541746 997 $aUNINA LEADER 03158nam 22006375 450 001 9910253964603321 005 20251113211650.0 010 $a3-319-28862-8 024 7 $a10.1007/978-3-319-28862-8 035 $a(CKB)3710000000602455 035 $a(EBL)4427532 035 $a(SSID)ssj0001654060 035 $a(PQKBManifestationID)16433925 035 $a(PQKBTitleCode)TC0001654060 035 $a(PQKBWorkID)14983050 035 $a(PQKB)10195654 035 $a(DE-He213)978-3-319-28862-8 035 $a(MiAaPQ)EBC4427532 035 $z(PPN)25886169X 035 $a(PPN)192219952 035 $a(EXLCZ)993710000000602455 100 $a20160223d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aHierarchical Modular Granular Neural Networks with Fuzzy Aggregation /$fby Daniela Sanchez, Patricia Melin 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (107 p.) 225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3712 300 $aIncludes index. 311 08$a3-319-28861-X 327 $aIntroduction -- Background and Theory -- Proposed Method -- Application to Human Recognition -- Experimental Results -- Conclusions. 330 $aIn this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus resulting in a new concept of MNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL) and hierarchical genetic algorithms (HGAs) are techniques used in this research work to improve results. These techniques are chosen because in other works have demonstrated to be a good option, and in the case of MNNs and HGAs, these techniques allow to improve the results obtained than with their conventional versions; respectively artificial neural networks and genetic algorithms. 410 0$aSpringerBriefs in Computational Intelligence,$x2625-3712 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aNeural networks (Computer science) 606 $aComputational Intelligence 606 $aArtificial Intelligence 606 $aMathematical Models of Cognitive Processes and Neural Networks 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aNeural networks (Computer science) 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 676 $a620 700 $aSanchez$b Daniela$4aut$4http://id.loc.gov/vocabulary/relators/aut$0762262 702 $aMelin$b Patricia$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910253964603321 996 $aHierarchical Modular Granular Neural Networks with Fuzzy Aggregation$92496947 997 $aUNINA