LEADER 03217nam 2200553 450 001 9910796608603321 005 20230124195315.0 010 $a3-11-044967-6 024 7 $a10.1515/9783110450293 035 $a(CKB)4100000001502389 035 $a(DE-B1597)458186 035 $a(OCoLC)1020032690 035 $a(DE-B1597)9783110450293 035 $a(Au-PeEL)EBL5158279 035 $a(CaPaEBR)ebr11497598 035 $a(CaSebORM)9783110449679 035 $a(MiAaPQ)EBC5158279 035 $a(EXLCZ)994100000001502389 100 $a20180206h20182018 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 00$aBlind equalization in neural networks $etheory, algorithms and applications /$fLiyi Zhang [and three others] 210 1$aBerlin, [Germany] ;$aBoston, [Massachusetts] :$cDe Gruyter :$cTsinghua University Press,$d2018. 210 4$dİ2018 215 $a1 online resource (268 pages) $cillustrations 311 $a3-11-044962-5 311 $a3-11-045029-1 320 $aIncludes bibliographical references and index. 327 $tFrontmatter -- $tPreface -- $tContents -- $t1. Introduction -- $t2. The Fundamental Theory of Neural Network Blind Equalization Algorithm -- $t3. Research of Blind Equalization Algorithms Based on FFNN -- $t4. Research of Blind Equalization Algorithms Based on the FBNN -- $t5. Research of Blind Equalization Algorithms Based on FNN -- $t6. Blind Equalization Algorithm Based on Evolutionary Neural Network -- $t7. Blind equalization Algorithm Based on Wavelet Neural Network -- $t8. Application of Neural Network Blind Equalization Algorithm in Medical Image Processing -- $tAppendix A: Derivation of the Hidden Layer Weight Iterative Formula in the Blind Equalization Algorithm Based on the Complex Three-Layer FFNN -- $tAppendix B: Iterative Formulas Derivation of Complex Blind Equalization Algorithm Based on BRNN -- $tAppendix C: Types of Fuzzy Membership Function -- $tAppendix D: Iterative Formula Derivation of Blind Equalization Algorithm Based on DRFNN -- $tReferences -- $tIndex 330 $aThe book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms in recurrent networks, fuzzy networks and other frequently-studied neural networks. Each algorithm is accompanied by derivation, modeling and simulation, making the book an essential reference for electrical engineers, computer intelligence researchers and neural scientists. 606 $aNeural networks (Computer science)$vCongresses 606 $aNeural networks (Computer science)$xScientific applications 606 $aNeural networks (Computer science) 615 0$aNeural networks (Computer science) 615 0$aNeural networks (Computer science)$xScientific applications. 615 0$aNeural networks (Computer science) 676 $a006.32 700 $aZhang$b Liyi$01487205 702 $aZhang$b Liyi 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910796608603321 996 $aBlind equalization in neural networks$93706976 997 $aUNINA