Feed-Forward Neural Networks, 1995 Vector Decomposition Analysis, Modelling and Analog Implementation The Springer International Series in Engineering and Computer Science Series, Vol. 314
Auteur : Annema Jouke
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.
Date de parution : 07-2013
Ouvrage de 238 p.
15.5x23.5 cm
Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).
Prix indicatif 105,49 €
Ajouter au panierMots-clés :
Hardware; Signal; analog; behavior; learning; modeling; network; neural networks; perception