Feed-Forward Neural Networks : Vector Decomposition Analysis, Modelling and Analog Implementation /
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 hardw...
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| Format: | eBook |
| Language: | English |
| Published: |
Boston, MA :
Springer US,
1995.
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| Series: | International series in engineering and computer science ;
314. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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. |
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| Item Description: | Electronic resource. |
| Physical Description: | 1 online resource (256 pages) |
| ISBN: | 9781461523376 (electronic bk.) 1461523370 (electronic bk.) |
| ISSN: | 0893-3405 ; |