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

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£109.99
Paperback 238 Pages / Published: 13/07/2013
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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.

Publisher: Springer-Verlag New York Inc.
ISBN: 9781461359906
Number of pages: 238
Weight: 397 g
Dimensions: 235 x 155 x 14 mm
Edition: Softcover reprint of the original 1st ed. 199

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