LEADER 05295nam 2200697Ia 450 001 9910782118803321 005 20230124182720.0 010 $a1-281-93438-0 010 $a9786611934385 010 $a981-279-421-2 035 $a(CKB)1000000000537794 035 $a(EBL)1679483 035 $a(OCoLC)879074229 035 $a(SSID)ssj0000160393 035 $a(PQKBManifestationID)11154066 035 $a(PQKBTitleCode)TC0000160393 035 $a(PQKBWorkID)10183708 035 $a(PQKB)10226146 035 $a(MiAaPQ)EBC1679483 035 $a(WSP)00005493 035 $a(Au-PeEL)EBL1679483 035 $a(CaPaEBR)ebr10255510 035 $a(CaONFJC)MIL193438 035 $a(EXLCZ)991000000000537794 100 $a20040907d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aFuzzy neural network theory and application$b[electronic resource] /$fPuyin Liu, Hongxing Li 210 $aRiver Edge, NJ $cWorld Scientific$dc2004 215 $a1 online resource (395 p.) 225 1 $aSeries in machine perception and artificial intelligence ;$vv. 59 300 $aDescription based upon print version of record. 311 $a981-238-786-2 320 $aIncludes bibliographical references and index. 327 $aContents ; Foreword ; Preface ; Chapter I Introduction ; S1.1 Classification of fuzzy neural networks ; S1.2 Fuzzy neural networks with fuzzy operators ; S1.3 Puzzified neural networks ; 1.3.1 Learning algorithm for regular FNN's ; 1.3.2 Universal approximation of regular FNN's 327 $aS1.4 Fuzzy systems and fuzzy inference networks 1.4.1 Fuzzy systems ; 1.4.2 Fuzzy inference networks ; S1.5 Fuzzy techniques in image restoration ; 1.5.1 Crisp nonlinear filters ; 1.5.2 Fuzzy filters ; S1.6 Notations and preliminaries ; S1.7 Outline of the topics of the chapters 327 $aReferences Chapter II Fuzzy Neural Networks for Storing and Classifying ; S2.1 Two layer max-min fuzzy associative memory ; 2.1.1 FAM with threshold ; 2.1.2 Simulation example ; S2.2 Fuzzy 6-learning algorithm ; 2.2.1 FAM's based on 'V - /\' ; 2.2.2 FAM's based on 'V - *' 327 $aS2.3 BP learning algorithm of FAM's 2.3.1 Two analytic functions ; 2.3.2 BP learning algorithm ; S2.4 Fuzzy ART and fuzzy ARTMAP ; 2.4.1 ART1 architecture ; 2.4.2 Fuzzy ART ; 2.4.3 Fuzzy ARTMAP ; 2.4.4 Real examples ; References 327 $aChapter III Fuzzy Associative Memory-Feedback Networks S3.1 Fuzzy Hopfield networks ; 3.1.1 Attractor and attractive basin ; 3.1.2 Learning algorithm based on fault-tolerance ; 3.1.3 Simulation example ; S3.2 Fuzzy Hopfield network with threshold ; 3.2.1 Attractor and stability 327 $a3.2.2 Analysis of fault-tolerance 330 $a This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to h 410 0$aSeries in machine perception and artificial intelligence ;$vv. 59. 606 $aNeural networks (Computer science) 606 $aFuzzy systems 615 0$aNeural networks (Computer science) 615 0$aFuzzy systems. 676 $a006.32 686 $a54.72$2bcl 686 $a31.10$2bcl 700 $aLiu$b Puyin$01531933 701 $aLi$b Hong-Xing$f1953-$01531934 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910782118803321 996 $aFuzzy neural network theory and application$93777909 997 $aUNINA