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Principles of artificial neural networks /
The 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 networ...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New Jersey :
World Scientific,
©2007.
|
| Edición: | 2nd ed. |
| Colección: | Advanced series on circuits and systems ;
v. 6. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Graupe, Daniel. | |
| 245 | 1 | 0 | |a Principles of artificial neural networks / |c Daniel Graupe. |
| 250 | |a 2nd ed. | ||
| 260 | |a New Jersey : |b World Scientific, |c ©2007. | ||
| 300 | |a 1 online resource (xv, 303 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
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| 490 | 1 | |a Advanced series on circuits and systems ; |v vol. 6 | |
| 504 | |a Includes bibliographical references (pages 291-297) and indexes. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Acknowledgments; Preface to the First Edition; Preface to the Second Edition; Contents; Chapter 1. Introduction and Role of Arti cial 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. Hop eld 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. | |
| 505 | 8 | |a Chapter 13. Large Scale Memory Storage and Retrieval (LAMSTAR) NetworkProblems; References; Author Index; Subject Index. | |
| 520 | |a The 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. | ||
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| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
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| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast |0 (OCoLC)fst01036260 | |
| 776 | 0 | 8 | |i Print version: |a Graupe, Daniel. |t Principles of artificial neural networks. |b 2nd ed. |d New Jersey : World Scientific, ©2007 |z 9812706240 |z 9789812706249 |w (OCoLC)141384911 |
| 830 | 0 | |a Advanced series on circuits and systems ; |v v. 6. | |
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