Feed-Forward Neural Networks : Vector Decomposition Analysis, Modelling and Analog Implementation

Jouke Annema

ISBN 10: 0792395670 / ISBN 13: 9780792395676

Editore: Springer US, Springer US, 1995

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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.

Codice articolo 9780792395676

Dati bibliografici

• Titolo: Feed-Forward Neural Networks : Vector Decomposition Analysis, Modelling and Analog Implementation
• Editore: Springer US, Springer US
• Data di pubblicazione: 1995
• Lingua: Inglese
• ISBN 10: 0792395670
• ISBN 13: 9780792395676
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 160 A x 19 P (millimetri) 241 millimetri di larghezza per 160 millimetri di altezza per 19 millimetri di profondità
• Peso: 553 grammi

Descrizione articolo

Riassunto

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.

Contenuti

Foreword. 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 Requirement 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. Index. Nomenclature.