| Record ID | marc_columbia/Columbia-extract-20221130-031.mrc:65239466:3732 |
| Source | marc_columbia |
| Download Link | /show-records/marc_columbia/Columbia-extract-20221130-031.mrc:65239466:3732?format=raw |
LEADER: 03732cam a2200673 a 4500
001 15076410
005 20210607125955.0
006 m o d
007 cr cnu---unuuu
008 090928s2010 flua ob 001 0 eng c
035 $a(OCoLC)ocn440707529
035 $a(NNC)15076410
040 $aN$T$beng$epn$cN$T$dWAU$dOKU$dOCLCQ$dIDEBK$dOCLCQ$dUIU$dOCLCQ$dCDX$dOCLCQ$dOCLCF$dOCLCO$dCRCPR$dOCLCQ$dYDXCP$dOCLCQ$dPIFBY$dOTZ$dOCLCQ$dUAB$dERL$dOCLCQ$dCEF$dUPM$dNLE$dYDX$dAU@$dUKMGB$dWYU$dS9I$dLEAUB$dZCU
015 $aGBB7B0361$2bnb
016 7 $a018393255$2Uk
019 $a449519916$a992049596$a994975108$a1031055168$a1062929830$a1086910780
020 $a9781420007763$q(electronic bk.)
020 $a1420007769$q(electronic bk.)
020 $z9780849375538$q(alk. paper)
020 $z0849375533$q(alk. paper)
020 $z9781138112056
024 7 $a10.1201/9781315221755$2doi
035 $a(OCoLC)440707529$z(OCoLC)449519916$z(OCoLC)992049596$z(OCoLC)994975108$z(OCoLC)1031055168$z(OCoLC)1062929830$z(OCoLC)1086910780
037 $a229380$bMIL
042 $apcc
050 4 $aTJ217.5$b.W355 2010
072 7 $aTEC$x004000$2bisacsh
072 7 $aTEC$x037000$2bisacsh
082 04 $a629.8$222
049 $aZCUA
100 1 $aWang, Cong.
245 10 $aDeterministic learning theory for identification, recognition, and control /$cCong Wang, David J. Hill.
250 $a1st ed.
260 $aBoca Raton :$bCRC Press,$c©2010.
300 $a1 online resource (xix, 195 pages) :$billustrations.
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
347 $adata file$2rda
490 1 $aAutomation and control engineering ;$v32
504 $aIncludes bibliographical references and index.
505 0 $aIntroduction -- RBF network approximation and persistence of excitation -- The deterministic learning mechanism -- Deterministic learning from closed-loop control -- Dynamical pattern recognition -- Pattern-based intelligent control -- Deterministic learning with output measurements -- Toward human-like learning and control.
588 0 $aPrint version record.
520 $aDeterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way.
650 0 $aIntelligent control systems.
650 0 $aNeural networks (Computer science)
650 0 $aControl theory.
650 7 $aTECHNOLOGY & ENGINEERING$xAutomation.$2bisacsh
650 7 $aTECHNOLOGY & ENGINEERING$xRobotics.$2bisacsh
650 7 $aControl theory.$2fast$0(OCoLC)fst00877085
650 7 $aIntelligent control systems.$2fast$0(OCoLC)fst00975911
650 7 $aNeural networks (Computer science)$2fast$0(OCoLC)fst01036260
655 4 $aElectronic books.
655 0 $aElectronic books.
700 1 $aHill, David J.$q(David John),$d1949-
776 08 $iPrint version:$aWang, Cong.$tDeterministic learning theory for identification, recognition, and control.$b1st ed.$dBoca Raton : CRC Press, ©2010$z9780849375538$z0849375533$w(DLC) 2008038057$w(OCoLC)249133624
830 0 $aAutomation and control engineering ;$v32.
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15076410$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS