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Feed-Forward Neural Networks

Vector Decomposition Analysis, Modelling and Analog Implementation

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Paperback / softback
10 November 2013
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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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    Product Details
    ISBN-13
    9781461359906
    Author
    Jouke Annema
    Publisher
    Springer Nature B.V.
    Published
    10 November 2013
    Format
    Paperback / softback

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    RRP: $185.90
    $183.11
    Ships in 3-5 business days

    Delivery estimate

    Hurry up! Current stock:

    Feed-Forward Neural Networks

    RRP: $185.90
    $183.11

    Description

    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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