The Promise of Neural Networks (Perspectives in Neural Computing)

Taylor, J.G.

ISBN 10: 3540197737 / ISBN 13: 9783540197737

Editore: Springer, 1993

Riguardo questo articolo

Like New.

Codice articolo ERICA78735401977376

Dati bibliografici

• Titolo: The Promise of Neural Networks (Perspectives in Neural Computing)
• Editore: Springer
• Data di pubblicazione: 1993
• Lingua: Inglese
• ISBN 10: 3540197737
• ISBN 13: 9783540197737
• Rilegatura: Paperback
• Condizione: Like New
• Tipo di libro: book

Descrizione articolo

Riassunto

This book is the product of a 15-month intensive investigation of the European artificial network scene, together with a view of the broader framework of the subject in a world context. It could not have been completed in such a remarkably short time, and so effectively, without the dedicated efforts of Louise Turner, the DEANNA secretary, and Geoff Chappell, the DEANNA researcher, at the Centre for Neural Networks, King's College, London. I would like to take this opportunity to thank them for their heroic efforts. I would also like to thank my colleagues in the Centre and in the Mathematics Department, especially Mark Plumbley, Michael Reiss and Trevor Clarkson for all their help and encouragement, Denise Gorse of University College London, for allowing use of her lecture notes as a basis for the tutorial and the DEANNA partners for the part they played. Finally I would like to acknowledge the European Community support, and especially Mike Coyle for his trenchant comments during the carrying out of the work. March 1993 J. G. Taylor CONTENTS PART I: SETTING THE SCENE Chapter 1: DEANNA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1 . 1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 . 2 The Geographical Dimension. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1 1. 3 The Industrial Dimension. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1 . 4 The Plan for Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Chapter 2: Neural Net Demonstrators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2. 1 The Status of Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2. 2 Reasons for the Employment of Neural Networks . . . . . . . . . . . . . . . . . . . 9 2. 3 Neural Network Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2. 4 Areas of Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2. 5 Typical Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Contenuti

I: Setting the Scene.- 1: DEANNA.- 1.1 Introduction.- 1.2 The Geographical Dimension.- 1.3 The Industrial Dimension.- 1.4 The Plan for Neural Networks.- 2: Neural Net Demonstrators.- 2.1 The Status of Neural Networks.- 2.2 Reasons for the Employment of Neural Networks.- 2.3 Neural Network Models.- 2.4 Areas of Application.- 2.5 Typical Applications.- 2.6 Devices for Running Neural Nets.- 2.7 Applications References.- 3: Neural Net Simulators.- 3.1 Software Simulators.- 3.2 Hardware Simulators.- 3.3 Neural Network Chips.- II: Neural Networks Proper.- 4: Introduction to Neural Computing.- 4.1 History of Neurocomputing.- 4.2 Artificial Neural Networks (ANNs).- 4.3 The Biological Prototype.- 4.4 Types of Neural Nets.- 4.5 Learning Nets.- 4.6 Perceptron Learning.- 4.7 Limitations of Perceptrons.- 4.8 Backpropagation.- 4.9 Visualising Network Behaviour: the Error Weight Surface.- 4.10 The Hopfield Network.- 4.11 Topographic Maps.- 4.12 Reinforcement Training.- 4.13 Further Reading.- 4.14 Appendix.- 5: The Principles of Neural Networks.- 5.1 The Problem of Principles.- 5.2 Mathematical Approaches.- 5.3 Informational Approaches.- 5.4 Psychological Approaches.- 5.5 Logical Approaches.- 5.6 Genetic Approaches.- 5.7 References.- 6: Applications of Neural Networks.- 6.1 Introduction.- 6.2 Speech Recognition.- 6.3 Pattern Recognition.- 6.4 Medical Applications of Neural Networks.- 6.5 Control.- 6.6 Time Series.- 6.7 References.- III: The Future of Neural Networks.- 7: The DEANNA Database.- 7.1 The Questionnaire.- 7.2 Starting Up.- 7.3 Algorithms.- 7.4 Applications.- 7.5 Comments.- 7.6 References.- 7.7 Appendixes.- Appendix 1 The DEANNA Survey.- Appendix 2 DEANNA at ICANN ‘81.- 8: Whither Neural Networks?.- 8.1 Future Developments.- 8.2 Future Applications.- 8.3 Advisory Structure.- 8.4 The Future of Neural Networks.- 8.5 References.- 8.6 Appendixes.- Appendix 1 Large (8 or more) European Neural Network Groups.- Appendix 2 Networks of Excellence Proposal.- Appendix 3 Network Proposal on Theory of Neural Networks.- Glossary of Neural Network Terms.