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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.
is an excellent source of reference and may be used as a text for advanced courses.
Produkteigenschaften
- Artikelnummer: 9780792395676
- Medium: Buch
- ISBN: 978-0-7923-9567-6
- Verlag: Springer US
- Erscheinungstermin: 31.05.1995
- Sprache(n): Englisch
- Auflage: 1995
- Serie: The Springer International Series in Engineering and Computer Science
- Produktform: Gebunden
- Gewicht: 1190 g
- Seiten: 238
- Format (B x H x T): 160 x 241 x 19 mm
- Ausgabetyp: Kein, Unbekannt