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100   $a20080102d2007      uy             0
101 0 $aeng
135   $aurcn|||||||||
181   $ctxt
182   $cc
183   $acr
200 10$aPrinciples of artificial neural networks$b[electronic resource] /$fDaniel Graupe
205   $a2nd ed.
210   $aSingapore ;$aHackensack, N.J. $cWorld Scientific$dc2007
215   $a1 online resource (320 p.)
225 1 $aAdvanced series on circuits and systems ;$vvol. 6
300   $aDescription based upon print version of record.
311   $a981-270-624-0 
320   $aIncludes bibliographical references (p. 291-297) and indexes.
327   $aAcknowledgments; Preface to the First Edition; Preface to the Second Edition; Contents; Chapter 1. Introduction and Role of Artificial Neural Networks; Chapter 2. Fundamentals of Biological Neural Networks; Chapter 3. Basic Principles of ANNs and Their Early Structures; Chapter 4. The Perceptron; Chapter 5. The Madaline; Chapter 6. Back Propagation; Chapter 7. Hopeld Networks; Chapter 8. Counter Propagation; Chapter 9. Adaptive Resonance Theory; Chapter 10. The Cognitron and the Neocognitron; Chapter 11. Statistical Training; Chapter 12. Recurrent (Time Cycling) Back Propagation Networks
327   $aChapter 13. Large Scale Memory Storage and Retrieval (LAMSTAR) Network Problems; References; Author Index; Subject Index
330   $aThe book should serve as a text for a university graduate course or for an advanced undergraduate course on neural networks in engineering and computer science departments. It should also serve as a self-study course for engineers and computer scientists in the industry. Covering major neural network approaches and architectures with the theories, this text presents detailed case studies for each of the approaches, accompanied with complete computer codes and the corresponding computed results. The case studies are designed to allow easy comparison of network performance to illustrate strength
410  0$aAdvanced series on circuits and systems ;$vv. 6.
606   $aNeural networks (Computer science)
608   $aElectronic books.
615  0$aNeural networks (Computer science)
676   $a006.3/2
700   $aGraupe$b Daniel$014109
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910450698103321
996   $aPrinciples of artificial neural networks$92482699
997   $aUNINA