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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New Jersey :
World Scientific,
©2007.
|
| Ausgabe: | 2nd ed. |
| Schriftenreihe: | Advanced series on circuits and systems ;
v. 6. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource (xv, 303 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 291-297) and indexes. |
| ISBN: | 9789812770578 9812770577 |
Internformat
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| 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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| contents | 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. Chapter 13. Large Scale Memory Storage and Retrieval (LAMSTAR) NetworkProblems; References; Author Index; Subject Index. |
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| id | ZDB-4-EBU-ocn173684881 |
| illustrated | Illustrated |
| indexdate | 2024-11-26T14:48:58Z |
| institution | BVB |
| isbn | 9789812770578 9812770577 |
| language | English |
| oclc_num | 173684881 |
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| spelling | Graupe, Daniel. http://id.loc.gov/authorities/names/n81129377 Principles of artificial neural networks / Daniel Graupe. 2nd ed. New Jersey : World Scientific, ©2007. 1 online resource (xv, 303 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Advanced series on circuits and systems ; vol. 6 Includes bibliographical references (pages 291-297) and indexes. Print version record. 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. Chapter 13. Large Scale Memory Storage and Retrieval (LAMSTAR) NetworkProblems; References; Author Index; Subject Index. 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. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Réseaux neuronaux (Informatique) COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Neural networks (Computer science) fast Print version: Graupe, Daniel. Principles of artificial neural networks. 2nd ed. New Jersey : World Scientific, ©2007 9812706240 9789812706249 (OCoLC)141384911 Advanced series on circuits and systems ; v. 6. http://id.loc.gov/authorities/names/no94008328 FWS01 ZDB-4-EBU FWS_PDA_EBU https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=203908 Volltext |
| spellingShingle | Graupe, Daniel Principles of artificial neural networks / Advanced series on circuits and systems ; 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. Chapter 13. Large Scale Memory Storage and Retrieval (LAMSTAR) NetworkProblems; References; Author Index; Subject Index. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Réseaux neuronaux (Informatique) COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Neural networks (Computer science) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh90001937 |
| title | Principles of artificial neural networks / |
| title_auth | Principles of artificial neural networks / |
| title_exact_search | Principles of artificial neural networks / |
| title_full | Principles of artificial neural networks / Daniel Graupe. |
| title_fullStr | Principles of artificial neural networks / Daniel Graupe. |
| title_full_unstemmed | Principles of artificial neural networks / Daniel Graupe. |
| title_short | Principles of artificial neural networks / |
| title_sort | principles of artificial neural networks |
| topic | Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Réseaux neuronaux (Informatique) COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Neural networks (Computer science) fast |
| topic_facet | Neural networks (Computer science) Réseaux neuronaux (Informatique) COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. |
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