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Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation (The Springer International Series in Engineering and Computer Science, 314, Band 314) - Annema, Jouke
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Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation (The Springer International Series in Engineering and Computer Science, 314, Band 314) - copertina rigida, flessible

1995, ISBN: 9780792395676

Springer, Gebundene Ausgabe, Auflage: 1995, 251 Seiten, Publiziert: 1995-05-31T00:00:01Z, Produktgruppe: Buch, 2.62 kg, Hardware & Technik, Computer & Internet, Kategorien, Bücher, Lexika… Altro …

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Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation (The Springer International Series in Engineering and Computer Science, 314, Band 314) - Annema, Jouke
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Annema, Jouke:

Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation (The Springer International Series in Engineering and Computer Science, 314, Band 314) - copertina rigida, flessible

1995, ISBN: 9780792395676

Springer, Gebundene Ausgabe, Auflage: 1995, 251 Seiten, Publiziert: 1995-05-31T00:00:01Z, Produktgruppe: Buch, 2.62 kg, Hardware & Technik, Computer & Internet, Kategorien, Bücher, Lexika… Altro …

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Feed-Forward Neural Networks Vector Decomposition Analysis, Modelling and Analog Implementation - copertina rigida, flessible

1995

ISBN: 0792395670

1995 Gebundene Ausgabe Elektrotechnik, Statistik, Mathematische Physik, Schaltkreise und Komponenten (Bauteile), Hardware; Signal; Behavior; Learning; modeling; Network; neuralnetworks;… Altro …

Costi di spedizione:Versandkostenfrei innerhalb der BRD. (EUR 0.00) MARZIES.de Buch- und Medienhandel, 14621 Schönwalde-Glien
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Feed-Forward Neural Networks Vector Decomposition Analysis, Modelling and Analog Implementation - Annema, Jouke
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Annema, Jouke:
Feed-Forward Neural Networks Vector Decomposition Analysis, Modelling and Analog Implementation - copertina rigida, flessible

1995, ISBN: 0792395670

1995 Gebundene Ausgabe Elektrotechnik, Statistik, Mathematische Physik, Schaltkreise und Komponenten (Bauteile), Hardware; Signal; Behavior; Learning; modeling; Network; neuralnetworks;… Altro …

Costi di spedizione:Versandkostenfrei innerhalb der BRD. (EUR 0.00) MARZIES.de Buch- und Medienhandel, 14621 Schönwalde-Glien
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Feed-Forward Neural Networks - copertina rigida, flessible

1995, ISBN: 9780792395676

Vector Decomposition Analysis, Modelling and Analog Implementation, Buch, Hardcover, 1995 ed. [PU: Springer], Springer, 1995

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Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation (The Springer International Series in Engineering and Computer Science, 314, Band 314)
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ISBN:

9780792395676


Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation presents a novel method for the mathematical analysis of neural networks that learn according to the back-propagation algorithm. The book also discusses some other recent alternative algorithms for hardware implemented perception-like neural networks. The method permits a simple analysis of the learning behaviour of neural networks, allowing specifications for their building blocks to be readily obtained. Starting with the derivation of a specification and ending with its hardware implementation, analog hard-wired, feed-forward neural networks with on-chip back-propagation learning are designed in their entirety. On-chip learning is necessary in circumstances where fixed weight configurations cannot be used. It is also useful for the elimination of most mis-matches and parameter tolerances that occur in hard-wired neural network chips. Fully analog neural networks have several advantages over other implementations: low chip area, low power consumption, and high speed operation. Feed-Forward Neural Networks is an excellent source of reference and may be used as a text for advanced courses.

Informazioni dettagliate del libro - Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation (The Springer International Series in Engineering and Computer Science, 314, Band 314)


EAN (ISBN-13): 9780792395676
ISBN (ISBN-10): 0792395670
Copertina rigida
Anno di pubblicazione: 1995
Editore: Springer
256 Pagine
Peso: 0,549 kg
Lingua: eng/Englisch

Libro nella banca dati dal 2007-11-08T13:45:52+01:00 (Zurich)
Pagina di dettaglio ultima modifica in 2024-01-26T15:46:46+01:00 (Zurich)
ISBN/EAN: 9780792395676

ISBN - Stili di scrittura alternativi:
0-7923-9567-0, 978-0-7923-9567-6
Stili di scrittura alternativi e concetti di ricerca simili:
Titolo del libro: feed forward neural networks, vector analysis, internat, international engineering, the way forward, analog computer

Dati dell'editore

Autore: Jouke Annema
Titolo: The Springer International Series in Engineering and Computer Science; Feed-Forward Neural Networks - Vector Decomposition Analysis, Modelling and Analog Implementation
Editore: Springer; Springer US
238 Pagine
Anno di pubblicazione: 1995-05-31
New York; NY; US
Lingua: Inglese
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
Available
XIII, 238 p.

BB; Hardcover, Softcover / Technik/Elektronik, Elektrotechnik, Nachrichtentechnik; Schaltkreise und Komponenten (Bauteile); Verstehen; Hardware; Signal; analog; behavior; learning; modeling; network; neural networks; perception; Electronic Circuits and Systems; Electrical and Electronic Engineering; Complex Systems; Theoretical, Mathematical and Computational Physics; Elektrotechnik; Kybernetik und Systemtheorie; Mathematische Physik; BC

1 Introduction.- 2 The Vector Decomposition Method.- 3 Dynamics of Single Layer Nets.- 4 Unipolar Input Signals in Single-Layer Feed-Forward Neural Networks.- 5 Cross-talk in Single-Layer Feed-Forward Neural Networks.- 6 Precision Requirements for Analog Weight Adaptation Circuitry for Single-Layer Nets.- 7 Discretization of Weight Adaptations in Single-Layer Nets.- 8 Learning Behavior and Temporary Minima of Two-Layer Neural Networks.- 9 Biases and Unipolar Input signals for Two-Layer Neural Networks.- 10 Cost Functions for Two-Layer Neural Networks.- 11 Some issues for f’ (x).- 12 Feed-forward hardware.- 13 Analog weight adaptation hardware.- 14 Conclusions.- Nomenclature.

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