LEADER 03982nam 22006854a 450 001 9910809089503321 005 20240404143244.0 010 $a1-281-94787-3 010 $a9786611947873 010 $a981-279-685-1 035 $a(CKB)1000000000537914 035 $a(EBL)1679556 035 $a(OCoLC)879023757 035 $a(SSID)ssj0000209602 035 $a(PQKBManifestationID)11221394 035 $a(PQKBTitleCode)TC0000209602 035 $a(PQKBWorkID)10265972 035 $a(PQKB)10968413 035 $a(MiAaPQ)EBC1679556 035 $a(Au-PeEL)EBL1679556 035 $a(CaPaEBR)ebr10255722 035 $a(CaONFJC)MIL194787 035 $a(EXLCZ)991000000000537914 100 $a20030110d2003 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aNeural networks for intelligent signal processing /$fAnthony Zaknich 205 $a1st ed. 210 $aRiver Edge, NJ $cWorld Scientific$dc2003 215 $a1 online resource (510 p.) 225 1 $aSeries on innovative intelligence ;$vv. 4 300 $aDescription based upon print version of record. 311 $a981-238-305-0 320 $aIncludes bibliographical references and index. 327 $aContents; Acknowledgments; Foreword; Preface; 1. Introduction; 1.1 Motivation for ANNs; 1.2 ANN Definitions and Main Types; 1.3 Specific ANN Models; 1.4 ANN Black Box Model; 1.5 ANN Implementation; 1.6 When To Use an ANN; 1.7 How To Use an ANN 327 $a1.8 General Applications 1.9 Pattern Recognition Examples; 1.9.1 Sheep Eating Phase Identification from Jaw Sounds; 1.9.2 Particle Isolation in SEM Images; 1.9.3 Oxalate Needle Detection in Microscope Images ; 1.10 Function Mapping and Filtering Examples 327 $a1.10.1 Water Level from Resonant Sound Analysis 1.10.2 Nonlinear Signal Filtering; 1.11 Motor Control Example; 1.12 ANN Summary; References; 2. A Brief Historical Overview; 2.1 ANN History to 1970; 2.1.1 Key Events prior to 1970; 2.2 ANN History after 1970 327 $a2.2.1 Key Events after 1970 to the Mid 1980's 2.2.2 Developments after the Mid 1980's; 2.2.3 Nonparametric Learning From Finite Data; 2.3 Reasons for the Resurgence of Interest in ANNs; 2.4 Historical Summary ; References; 3. Basic Concepts; 3.1 The Basic Model of the Neuron 327 $a3.2 Activation Functions 3.3 Topologies; 3.4 Learning; 3.4.1 A Basic Supervised Learning Algorithm; 3.4.2 A Basic Unsupervised Learning Algorithm; 3.5 The Basic McCulloch Pitts and Perceptron Models; 3.6 Vectors Spaces and Matrix Models; 3.6.1 ANN Classifiers 327 $a3.6.2 Vectors and Feature Spaces 330 $aThis book provides a thorough theoretical and practical introduction to the application of neural networks to pattern recognition and intelligent signal processing. It has been tested on students, unfamiliar with neural networks, who were able to pick up enough details to successfully complete their masters or final year undergraduate projects. The text also presents a comprehensive treatment of a class of neural networks called common bandwidth spherical basis function NNs, including the probabilistic NN, the modified probabilistic NN and the general regression. Contents 410 0$aSeries on innovative intelligence ;$vv. 4. 606 $aNeural networks (Computer science) 606 $aSignal processing$xDigital techniques 606 $aIntelligent control systems 615 0$aNeural networks (Computer science) 615 0$aSignal processing$xDigital techniques. 615 0$aIntelligent control systems. 676 $a006.3/2 700 $aZaknich$b Anthony$01684420 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910809089503321 996 $aNeural networks for intelligent signal processing$94055907 997 $aUNINA