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Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation - Couverture rigide
Synopsis
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.
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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)
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Springer, 1995
ISBN 10 : 0792395670
ISBN 13 : 9780792395676
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Etat : Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions. N° de réf. du vendeur Z1-B-017-02011
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Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation (The Kluwer International Series in Engineering and Computer Science)
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ISBN 13 : 9780792395676
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Feed-Forward Neural Networks
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 a. N° de réf. du vendeur 5971589
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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)
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Feed-Forward Neural Networks : Vector Decomposition Analysis, Modelling and Analog Implementation
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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)
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Feed-Forward Neural Networks
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Springer US, Springer US Mai 1995, 1995
ISBN 10 : 0792395670
ISBN 13 : 9780792395676
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Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 256 pp. Englisch. N° de réf. du vendeur 9780792395676
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Feed-Forward Neural Networks : Vector Decomposition Analysis, Modelling and Analog Implementation
Edité par
Springer US, Springer US, 1995
ISBN 10 : 0792395670
ISBN 13 : 9780792395676
Neuf
Couverture rigide
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9780792395676
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Feed-Forward Neural Networks : Vector Decomposition Analysis, Modelling and Analog Implementation
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Feed-Forward Neural Networks
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Etat : New. pp. 256 Index. N° de réf. du vendeur 263058080
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