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...
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Singapore ; Hackensack, N.J. :
World Scientific,
©2007.
|
| Edition: | 2nd ed. |
| Series: | Advanced series on circuits and systems ;
v. 6. |
| Subjects: | |
| Online Access: |
Full text (Emmanuel users only) |
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 Singapore ; |a Hackensack, N.J. : |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. | ||
| 505 | 0 | |a Introduction and role of artificial neural networks -- Fundamentals of biological neural networks -- Basic principles of ANNs and their early structures -- The Perceptron -- The Madaline -- Back propagation -- Hopfield networks -- Counter propagation -- Adaptive resonance theory -- The Cognitron and the Neocognitron -- Statistical training -- Recurrent (Time cycling) back propagation networks -- Large scale memory storage and retrieval (LAMSTAR) network. | |
| 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. | ||
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 758 | |i has work: |a Principles of artificial neural networks (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGdjTJQMDpTFY94WcpYYxC |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Graupe, Daniel. |t Principles of artificial neural networks. |b 2nd ed. |d Singapore ; Hackensack, N.J. : World Scientific, ©2007 |w (DLC) 2008270006 |
| 830 | 0 | |a Advanced series on circuits and systems ; |v v. 6. | |
| 852 | |b Online |h ProQuest | ||
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