Jouke Annema

Feed-Forward Neural Networks

Vector Decomposition Analysis, Modelling and Analog Implementation

• Broschiertes Buch

Produktbeschreibung

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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Produktdetails

• The Springer International Series in Engineering and Computer Science 314
• Verlag: Springer / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 978-1-4613-5990-6
• Softcover reprint of the original 1st ed. 1995
• Seitenzahl: 256
• Erscheinungstermin: 13. Juli 2013
• Englisch
• Abmessung: 235mm x 155mm x 15mm
• Gewicht: 394g
• ISBN-13: 9781461359906
• ISBN-10: 1461359902
• Artikelnr.: 39916093

Inhaltsangabe

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.