| Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation Softcover Repri Edition Contributor(s): Annema, Jouke (Author) |
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ISBN: 1461359902 ISBN-13: 9781461359906 Publisher: Springer OUR PRICE: $104.49 Product Type: Paperback - Other Formats Published: July 2013 |
| Additional Information |
| BISAC Categories: - Technology & Engineering | Electronics - Circuits - General - Technology & Engineering | Electrical - Science | Physics - Mathematical & Computational |
| Dewey: 621 |
| Series: Springer International Series in Engineering and Computer Sc |
| Physical Information: 0.54" H x 6.14" W x 9.21" (0.80 lbs) 238 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |