03985nam 22006734a 450 991078228240332120230124182720.01-281-94787-39786611947873981-279-685-1(CKB)1000000000537914(EBL)1679556(OCoLC)879023757(SSID)ssj0000209602(PQKBManifestationID)11221394(PQKBTitleCode)TC0000209602(PQKBWorkID)10265972(PQKB)10968413(MiAaPQ)EBC1679556(Au-PeEL)EBL1679556(CaPaEBR)ebr10255722(CaONFJC)MIL194787(EXLCZ)99100000000053791420030110d2003 uy 0engurcn|||||||||txtccrNeural networks for intelligent signal processing[electronic resource] /Anthony ZaknichRiver Edge, NJ World Scientificc20031 online resource (510 p.)Series on innovative intelligence ;v. 4Description based upon print version of record.981-238-305-0 Includes bibliographical references and index.Contents; 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 ANN1.8 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 Examples1.10.1 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 19702.2.1 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 Neuron3.2 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 Classifiers3.6.2 Vectors and Feature SpacesThis 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. ContentsSeries on innovative intelligence ;v. 4.Neural networks (Computer science)Signal processingDigital techniquesIntelligent control systemsNeural networks (Computer science)Signal processingDigital techniques.Intelligent control systems.006.3/2Zaknich Anthony1563166MiAaPQMiAaPQMiAaPQBOOK9910782282403321Neural networks for intelligent signal processing3831359UNINA