portada Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation (en Inglés)

Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation (en Inglés)

Formato

Libro Físico

Autor

Jouke Annema

Editorial

Springer

Idioma

Inglés

N° páginas

238

Encuadernación

Tapa Blanda

Dimensiones

23.4x15.6x1.4 cm

Peso

0.36 kg.

ISBN13

9781461359906

Categorías

Física matemática
Ingeniería eléctrica
Electrónica: circuitos y componentes

Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation (en Inglés)

Jouke Annema (Autor) · Springer · Tapa Blanda

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Reseña del libro "Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation (en Inglés)"

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.