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Feed-Forward Neural Networks

Jouke Annema
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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.
      Format: Paperback / softback CONTRIBUTORS: Jouke Annema EAN: 9781461359906 COUNTRY: United States PAGES: WEIGHT: 397 g HEIGHT: 235 cm
      PUBLISHED BY: Springer-Verlag New York Inc. DATE PUBLISHED: 2013-07-13 CITY: GENRE: SCIENCE / Physics / Mathematical & Computational, SCIENCE / Physics / General, TECHNOLOGY & ENGINEERING / Electrical, TECHNOLOGY & ENGINEERING / Electronics / Circuits / General WIDTH: 155 cm SPINE:

      Book Themes:

      Cybernetics and systems theory, Mathematical physics, Electrical engineering, Electronics: circuits and components

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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.
      Format: Paperback / softback CONTRIBUTORS: Jouke Annema EAN: 9781461359906 COUNTRY: United States PAGES: WEIGHT: 397 g HEIGHT: 235 cm
      PUBLISHED BY: Springer-Verlag New York Inc. DATE PUBLISHED: 2013-07-13 CITY: GENRE: SCIENCE / Physics / Mathematical & Computational, SCIENCE / Physics / General, TECHNOLOGY & ENGINEERING / Electrical, TECHNOLOGY & ENGINEERING / Electronics / Circuits / General WIDTH: 155 cm SPINE:

      Book Themes:

      Cybernetics and systems theory, Mathematical physics, Electrical engineering, Electronics: circuits and components

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