Analogue imprecision in MLP training:
Hardware inaccuracy and imprecision are important considerations when implementing neural algorithms. This book presents a study of synaptic weight noise as a typical fault model for analogue VLSI realisations of MLP neural networks and examines the implications for learning and network performance....
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore
World Scientific Pub. Co.
c1996
|
| Schriftenreihe: | Progress in neural processing
4 |
| Schlagworte: | |
| Online-Zugang: | FHN01 Volltext |
| Zusammenfassung: | Hardware inaccuracy and imprecision are important considerations when implementing neural algorithms. This book presents a study of synaptic weight noise as a typical fault model for analogue VLSI realisations of MLP neural networks and examines the implications for learning and network performance. The aim of the book is to present a study of how including an imprecision model into a learning scheme as a "fault tolerance hint" can aid understanding of accuracy and precision requirements for a particular implementation. In addition the study shows how such a scheme can give rise to significant performance enhancement |
| Beschreibung: | xi, 178 p. ill |
| ISBN: | 9789812830012 |
Internformat
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Datensatz im Suchindex
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| author | Edwards, Peter J. |
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| discipline | Informatik |
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| id | DE-604.BV044636763 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:57:49Z |
| institution | BVB |
| isbn | 9789812830012 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030034736 |
| oclc_num | 1012710099 |
| open_access_boolean | |
| owner | DE-92 |
| owner_facet | DE-92 |
| physical | xi, 178 p. ill |
| psigel | ZDB-124-WOP ZDB-124-WOP FHN_PDA_WOP |
| publishDate | 1996 |
| publishDateSearch | 1996 |
| publishDateSort | 1996 |
| publisher | World Scientific Pub. Co. |
| record_format | marc |
| series2 | Progress in neural processing |
| spelling | Edwards, Peter J. Verfasser aut Analogue imprecision in MLP training Peter J. Edwards, Alan F. Murray Singapore World Scientific Pub. Co. c1996 xi, 178 p. ill txt rdacontent c rdamedia cr rdacarrier Progress in neural processing 4 Hardware inaccuracy and imprecision are important considerations when implementing neural algorithms. This book presents a study of synaptic weight noise as a typical fault model for analogue VLSI realisations of MLP neural networks and examines the implications for learning and network performance. The aim of the book is to present a study of how including an imprecision model into a learning scheme as a "fault tolerance hint" can aid understanding of accuracy and precision requirements for a particular implementation. In addition the study shows how such a scheme can give rise to significant performance enhancement Neural networks (Computer science) Perceptrons Murray, Alan F. Sonstige oth Erscheint auch als Druck-Ausgabe 9789810227395 Erscheint auch als Druck-Ausgabe 9810227396 http://www.worldscientific.com/worldscibooks/10.1142/3170#t=toc Verlag URL des Erstveroeffentlichers Volltext |
| spellingShingle | Edwards, Peter J. Analogue imprecision in MLP training Neural networks (Computer science) Perceptrons |
| title | Analogue imprecision in MLP training |
| title_auth | Analogue imprecision in MLP training |
| title_exact_search | Analogue imprecision in MLP training |
| title_full | Analogue imprecision in MLP training Peter J. Edwards, Alan F. Murray |
| title_fullStr | Analogue imprecision in MLP training Peter J. Edwards, Alan F. Murray |
| title_full_unstemmed | Analogue imprecision in MLP training Peter J. Edwards, Alan F. Murray |
| title_short | Analogue imprecision in MLP training |
| title_sort | analogue imprecision in mlp training |
| topic | Neural networks (Computer science) Perceptrons |
| topic_facet | Neural networks (Computer science) Perceptrons |
| url | http://www.worldscientific.com/worldscibooks/10.1142/3170#t=toc |
| work_keys_str_mv | AT edwardspeterj analogueimprecisioninmlptraining AT murrayalanf analogueimprecisioninmlptraining |