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: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore ; River Edge, NJ :
World Scientific,
©1996.
|
| Schriftenreihe: | Progress in neural processing ;
4. |
| Schlagworte: | |
| Online-Zugang: | 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: | 1 online resource (xi, 178 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 165-172) and index. |
| ISBN: | 9789812830012 9812830014 |
Internformat
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| 245 | 1 | 0 | |a Analogue imprecision in MLP training / |c Peter J. Edwards, Alan F. Murray. |
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| 504 | |a Includes bibliographical references (pages 165-172) and index. | ||
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| 505 | 0 | |a 1. Introduction. 1.1. Multi-layer perceptrons. 1.2. Stochastic systems. 1.3. Chapter summary -- 2. Neural network performance metrics. 2.1. Introduction. 2.2. Fault tolerance. 2.3. Generalisation. 2.4. Learning trajectory and speed. 2.5. Chapter summary -- 3. Noise in neural implementations. 3.1. Introduction. 3.2. Implementation errors. 3.3. An implementation error model. 3.4. The mathematical model. 3.5. Chapter summary -- 4. Simulation requirements and environment. 4.1. Introduction. 4.2. Simulation requirements. 4.3. Simulation environment. 4.4. Chapter summary -- 5. Fault tolerance. 5.1. Introduction. 5.2. Test environment. 5.3. Results. 5.4. Chapter summary -- 6. Generalisation ability. 6.1. Introduction. 6.2. Test environment. 6.3. Results. 6.4. Chapter summary -- 7. Learning trajectory and speed. 7.1. Introduction. 7.2. Test aims and method. 7.3. Results. 7.4. Chapter summary -- 8. Penalty terms for fault tolerance. 8.1. Introduction. 8.2. Definition of the penalty terms. 8.3. Learning algorithms. 8.4. Practical issues. 8.5. Chapter summary -- 9. Conclusions. 9.1. Introduction. 9.2. Implications for analogue hardware. 9.3. Synaptic noise as an enhancement scheme. 9.4. Learning hints. 9.5. General conclusions. | |
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| contents | 1. Introduction. 1.1. Multi-layer perceptrons. 1.2. Stochastic systems. 1.3. Chapter summary -- 2. Neural network performance metrics. 2.1. Introduction. 2.2. Fault tolerance. 2.3. Generalisation. 2.4. Learning trajectory and speed. 2.5. Chapter summary -- 3. Noise in neural implementations. 3.1. Introduction. 3.2. Implementation errors. 3.3. An implementation error model. 3.4. The mathematical model. 3.5. Chapter summary -- 4. Simulation requirements and environment. 4.1. Introduction. 4.2. Simulation requirements. 4.3. Simulation environment. 4.4. Chapter summary -- 5. Fault tolerance. 5.1. Introduction. 5.2. Test environment. 5.3. Results. 5.4. Chapter summary -- 6. Generalisation ability. 6.1. Introduction. 6.2. Test environment. 6.3. Results. 6.4. Chapter summary -- 7. Learning trajectory and speed. 7.1. Introduction. 7.2. Test aims and method. 7.3. Results. 7.4. Chapter summary -- 8. Penalty terms for fault tolerance. 8.1. Introduction. 8.2. Definition of the penalty terms. 8.3. Learning algorithms. 8.4. Practical issues. 8.5. Chapter summary -- 9. Conclusions. 9.1. Introduction. 9.2. Implications for analogue hardware. 9.3. Synaptic noise as an enhancement scheme. 9.4. Learning hints. 9.5. General conclusions. |
| ctrlnum | (OCoLC)846496133 |
| dewey-full | 006.3 |
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| discipline | Informatik |
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| id | ZDB-4-EBU-ocn846496133 |
| illustrated | Illustrated |
| indexdate | 2024-11-26T14:49:10Z |
| institution | BVB |
| isbn | 9789812830012 9812830014 |
| language | English |
| oclc_num | 846496133 |
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| physical | 1 online resource (xi, 178 pages) : illustrations |
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| series | Progress in neural processing ; |
| series2 | Progress in neural processing ; |
| spelling | Edwards, Peter J. (Peter John) https://id.oclc.org/worldcat/entity/E39PCjBvTF8wpjGHP7jpvqqcMX http://id.loc.gov/authorities/names/n96044908 Analogue imprecision in MLP training / Peter J. Edwards, Alan F. Murray. Singapore ; River Edge, NJ : World Scientific, ©1996. 1 online resource (xi, 178 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Progress in neural processing ; 4 Includes bibliographical references (pages 165-172) and index. Print version record. 1. Introduction. 1.1. Multi-layer perceptrons. 1.2. Stochastic systems. 1.3. Chapter summary -- 2. Neural network performance metrics. 2.1. Introduction. 2.2. Fault tolerance. 2.3. Generalisation. 2.4. Learning trajectory and speed. 2.5. Chapter summary -- 3. Noise in neural implementations. 3.1. Introduction. 3.2. Implementation errors. 3.3. An implementation error model. 3.4. The mathematical model. 3.5. Chapter summary -- 4. Simulation requirements and environment. 4.1. Introduction. 4.2. Simulation requirements. 4.3. Simulation environment. 4.4. Chapter summary -- 5. Fault tolerance. 5.1. Introduction. 5.2. Test environment. 5.3. Results. 5.4. Chapter summary -- 6. Generalisation ability. 6.1. Introduction. 6.2. Test environment. 6.3. Results. 6.4. Chapter summary -- 7. Learning trajectory and speed. 7.1. Introduction. 7.2. Test aims and method. 7.3. Results. 7.4. Chapter summary -- 8. Penalty terms for fault tolerance. 8.1. Introduction. 8.2. Definition of the penalty terms. 8.3. Learning algorithms. 8.4. Practical issues. 8.5. Chapter summary -- 9. Conclusions. 9.1. Introduction. 9.2. Implications for analogue hardware. 9.3. Synaptic noise as an enhancement scheme. 9.4. Learning hints. 9.5. General conclusions. 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) http://id.loc.gov/authorities/subjects/sh90001937 Perceptrons. http://id.loc.gov/authorities/subjects/sh85099714 Réseaux neuronaux (Informatique) Perceptrons. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Neural networks (Computer science) fast Perceptrons fast Murray, Alan F. Print version: Edwards, Peter J. (Peter John). Analogue imprecision in MLP training. Singapore ; River Edge, NJ : World Scientific, ©1996 9810227396 (DLC) 96008889 (OCoLC)34710796 Progress in neural processing ; 4. http://id.loc.gov/authorities/names/no95053043 FWS01 ZDB-4-EBU FWS_PDA_EBU https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=564397 Volltext |
| spellingShingle | Edwards, Peter J. (Peter John) Analogue imprecision in MLP training / Progress in neural processing ; 1. Introduction. 1.1. Multi-layer perceptrons. 1.2. Stochastic systems. 1.3. Chapter summary -- 2. Neural network performance metrics. 2.1. Introduction. 2.2. Fault tolerance. 2.3. Generalisation. 2.4. Learning trajectory and speed. 2.5. Chapter summary -- 3. Noise in neural implementations. 3.1. Introduction. 3.2. Implementation errors. 3.3. An implementation error model. 3.4. The mathematical model. 3.5. Chapter summary -- 4. Simulation requirements and environment. 4.1. Introduction. 4.2. Simulation requirements. 4.3. Simulation environment. 4.4. Chapter summary -- 5. Fault tolerance. 5.1. Introduction. 5.2. Test environment. 5.3. Results. 5.4. Chapter summary -- 6. Generalisation ability. 6.1. Introduction. 6.2. Test environment. 6.3. Results. 6.4. Chapter summary -- 7. Learning trajectory and speed. 7.1. Introduction. 7.2. Test aims and method. 7.3. Results. 7.4. Chapter summary -- 8. Penalty terms for fault tolerance. 8.1. Introduction. 8.2. Definition of the penalty terms. 8.3. Learning algorithms. 8.4. Practical issues. 8.5. Chapter summary -- 9. Conclusions. 9.1. Introduction. 9.2. Implications for analogue hardware. 9.3. Synaptic noise as an enhancement scheme. 9.4. Learning hints. 9.5. General conclusions. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Perceptrons. http://id.loc.gov/authorities/subjects/sh85099714 Réseaux neuronaux (Informatique) Perceptrons. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Neural networks (Computer science) fast Perceptrons fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh85099714 |
| 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) http://id.loc.gov/authorities/subjects/sh90001937 Perceptrons. http://id.loc.gov/authorities/subjects/sh85099714 Réseaux neuronaux (Informatique) Perceptrons. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Neural networks (Computer science) fast Perceptrons fast |
| topic_facet | Neural networks (Computer science) Perceptrons. Réseaux neuronaux (Informatique) COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Perceptrons |
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