LEADER 02432nas 2200637- 450 001 996336138303316 005 20230204213019.0 035 $a(OCoLC)842836133 035 $a(CKB)2560000000085614 035 $a(CONSER)--2016236837 035 $a(EXLCZ)992560000000085614 100 $a20130513b20122017 --- - 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aJournal of research and practice for adult literacy, secondary, and basic education 210 1$aSyracuse, NY :$cCommission on Adult Basic Education (COABE) ;$aBowling Green, KY :$cWestern Kentucky University,$d2012- 210 31$a[Bradenton, FL?] :$cCoalition on Adult Basic Education ;$a[New Brunswick, N.J.] :$aRutgers University 215 $a1 online resource 300 $aRefereed/Peer-reviewed 311 $a2169-0480 517 3 $aCOABE journal 606 $aAdult education$zUnited States$vPeriodicals 606 $aElementary education of adults$zUnited States$vPeriodicals 606 $aFunctional literacy$vPeriodicals 606 $aÉducation des adultes$zÉtats-Unis$vPériodiques 606 $aAdultes$xEnseignement primaire$zÉtats-Unis$vPériodiques 606 $aAlphabétisation fonctionnelle$vPériodiques 606 $aAdult education$2fast$3(OCoLC)fst00797275 606 $aElementary education of adults$2fast$3(OCoLC)fst00907873 606 $aFunctional literacy$2fast$3(OCoLC)fst00936073 607 $aUnited States$2fast 607 $aVerenigde Staten$2gtt 608 $aPeriodicals.$2fast 608 $aPeriodicals.$2lcgft 615 0$aAdult education 615 0$aElementary education of adults 615 0$aFunctional literacy 615 6$aÉducation des adultes 615 6$aAdultes$xEnseignement primaire 615 6$aAlphabétisation fonctionnelle 615 7$aAdult education. 615 7$aElementary education of adults. 615 7$aFunctional literacy. 676 $a374 686 $a79.63$2bcl 712 02$aCommission on Adult Basic Education (U.S.) 712 02$aWestern Kentucky University. 712 02$aCoalition on Adult Basic Education (U.S.), 712 02$aRutgers University, 906 $aJOURNAL 912 $a996336138303316 996 $aJournal of research and practice for adult literacy, secondary, and basic education$92351268 997 $aUNISA LEADER 04758nam 22006975 450 001 9910299483703321 005 20251117071858.0 010 $a9783319024721 010 $a3319024728 024 7 $a10.1007/978-3-319-02472-1 035 $a(CKB)3710000000078600 035 $a(DE-He213)978-3-319-02472-1 035 $a(SSID)ssj0001066947 035 $a(PQKBManifestationID)11600860 035 $a(PQKBTitleCode)TC0001066947 035 $a(PQKBWorkID)11079730 035 $a(PQKB)10506639 035 $a(MiAaPQ)EBC3092048 035 $a(PPN)176106111 035 $a(EXLCZ)993710000000078600 100 $a20131112d2014 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Organic Networks $eArtificial Intelligence Based on Carbon Networks /$fby Hiram Ponce-Espinosa, Pedro Ponce-Cruz, Arturo Molina 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (XII, 228 p. 192 illus., 56 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v521 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a9783319024714 311 08$a331902471X 327 $aIntroduction to Modeling Problems -- Chemical Organic Compounds -- Artificial Organic Networks -- Artificial Hydrocarbon Networks -- Enhancements of Artificial Hydrocarbon Networks -- Notes on Modeling Problems Using Artificial Hydrocarbon Networks -- Applications of Artificial Hydrocarbon Networks.-Appendices. 330 $aThis monograph describes the synthesis and use of biologically-inspired artificial hydrocarbon networks (AHNs) for approximation models associated with machine learning and a novel computational algorithm with which to exploit them. The reader is first introduced to various kinds of algorithms designed to deal with approximation problems and then, via some conventional ideas of organic chemistry, to the creation and characterization of artificial organic networks and AHNs in particular. The advantages of using organic networks are discussed with the rules to be followed to adapt the network to its objectives. Graph theory is used as the basis of the necessary formalism. Simulated and experimental examples of the use of fuzzy logic and genetic algorithms with organic neural networks are presented and a number of modeling problems suitable for treatment by AHNs are described: ·        approximation; ·        inference; ·        clustering; ·        control; ·        classification; and ·        audio-signal filtering. The text finishes with a consideration of directions in which AHNs  could be implemented and developed in future. A complete LabVIEW? toolkit, downloadable from the book?s page at springer.com enables readers to design and implement organic neural networks of their own. The novel approach to creating networks suitable for machine learning systems demonstrated in Artificial Organic Networks will be of interest to academic researchers and graduate students working in areas associated with computational intelligence, intelligent control, systems approximation and complex networks. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v521 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aBiochemical engineering 606 $aComputer simulation 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aBiochemical Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/C12029 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aBiochemical engineering. 615 0$aComputer simulation. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aBiochemical Engineering. 615 24$aSimulation and Modeling. 676 $a006.3 700 $aPonce-Espinosa$b Hiram$4aut$4http://id.loc.gov/vocabulary/relators/aut$0989119 702 $aPonce Cruz$b Pedro$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aMolina$b Arturo$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910299483703321 996 $aArtificial Organic Networks$92262118 997 $aUNINA