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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boston, MA
Springer US
1995
|
| Series: | The Springer International Series in Engineering and Computer Science
314 |
| Subjects: | |
| Online Access: | BTU01 Volltext |
| 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 |
| Physical Description: | 1 Online-Ressource (XIII, 238 p) |
| ISBN: | 9781461523376 |
| DOI: | 10.1007/978-1-4615-2337-6 |
Staff View
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| spelling | Annema, Anne-Johan Verfasser aut Feed-Forward Neural Networks Vector Decomposition Analysis, Modelling and Analog Implementation by Anne-Johan Annema Boston, MA Springer US 1995 1 Online-Ressource (XIII, 238 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science 314 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 Engineering Circuits and Systems Electrical Engineering Statistical Physics, Dynamical Systems and Complexity Statistical physics Dynamical systems Electrical engineering Electronic circuits Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Backpropagation-Algorithmus (DE-588)4354627-4 gnd rswk-swf 1\p (DE-588)4113937-9 Hochschulschrift gnd-content Neuronales Netz (DE-588)4226127-2 s Backpropagation-Algorithmus (DE-588)4354627-4 s 2\p DE-604 Erscheint auch als Druck-Ausgabe 9781461359906 https://doi.org/10.1007/978-1-4615-2337-6 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Annema, Anne-Johan Feed-Forward Neural Networks Vector Decomposition Analysis, Modelling and Analog Implementation Engineering Circuits and Systems Electrical Engineering Statistical Physics, Dynamical Systems and Complexity Statistical physics Dynamical systems Electrical engineering Electronic circuits Neuronales Netz (DE-588)4226127-2 gnd Backpropagation-Algorithmus (DE-588)4354627-4 gnd |
| subject_GND | (DE-588)4226127-2 (DE-588)4354627-4 (DE-588)4113937-9 |
| title | Feed-Forward Neural Networks Vector Decomposition Analysis, Modelling and Analog Implementation |
| title_auth | Feed-Forward Neural Networks Vector Decomposition Analysis, Modelling and Analog Implementation |
| title_exact_search | Feed-Forward Neural Networks Vector Decomposition Analysis, Modelling and Analog Implementation |
| title_full | Feed-Forward Neural Networks Vector Decomposition Analysis, Modelling and Analog Implementation by Anne-Johan Annema |
| title_fullStr | Feed-Forward Neural Networks Vector Decomposition Analysis, Modelling and Analog Implementation by Anne-Johan Annema |
| title_full_unstemmed | Feed-Forward Neural Networks Vector Decomposition Analysis, Modelling and Analog Implementation by Anne-Johan Annema |
| title_short | Feed-Forward Neural Networks |
| title_sort | feed forward neural networks vector decomposition analysis modelling and analog implementation |
| title_sub | Vector Decomposition Analysis, Modelling and Analog Implementation |
| topic | Engineering Circuits and Systems Electrical Engineering Statistical Physics, Dynamical Systems and Complexity Statistical physics Dynamical systems Electrical engineering Electronic circuits Neuronales Netz (DE-588)4226127-2 gnd Backpropagation-Algorithmus (DE-588)4354627-4 gnd |
| topic_facet | Engineering Circuits and Systems Electrical Engineering Statistical Physics, Dynamical Systems and Complexity Statistical physics Dynamical systems Electrical engineering Electronic circuits Neuronales Netz Backpropagation-Algorithmus Hochschulschrift |
| url | https://doi.org/10.1007/978-1-4615-2337-6 |
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