Mathematical approaches to neural networks
Mathematical approaches to neural networks [electronic resource] /
edited by J.G. Taylor.
- Amsterdam ; New York : North-Holland, 1993.
- 1 online resource (vii, 382 p.) : ill.
- North-Holland mathematical library ; v. 51 .
- North-Holland mathematical library ; v. 51. .
Includes bibliographical references.
Front Cover; Mathematical Approaches to Neural Networks, Volume 51; Copyright Page; Preface; Table of Contents; Chapter 1. Control Theory Approach; Chapter 2. Computational Learning Theory for Artificial Neural Networks; Chapter 3. Time-summating Network Approach; Chapter 4. The Numerical Analysis Approach; Chapter 5. Self-organising Neural Networks for Stable Control of Autonomous Behavior in a Changing World; Chapter 6. On-line Learning Processes in Artificial Neural Networks; Chapter 7. Multilayer Functionals; Chapter 8. Neural Networks: The Spin Glass Approach
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The subject of Neural Networks is being seen to be coming of age, after its initial inception 50 years ago in the seminal work of McCulloch and Pitts. It is proving to be valuable in a wide range of academic disciplines and in important applications in industrial and business tasks. The progress being made in each approach is considerable. Nevertheless, both stand in need of a theoretical framework of explanation to underpin their usage and to allow the progress being made to be put on a firmer footing. This book aims to strengthen the foundations in its presentation of mathematical approaches to neural networks. It is through these that a suitable explanatory framework is expected to be found. The approaches span a broad range, from single neuron details to numerical analysis, functional analysis and dynamical systems theory. Each of these avenues provides its own insights into the way neural networks can be understood, both for artificial ones and simplified simulations. As a whole, the publication underlines the importance of the ever-deepening mathematical understanding of neural networks.
Electronic reproduction.
[S.l.] :
HathiTrust Digital Library,
2010.
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.
http://purl.oclc.org/DLF/benchrepro0212
9780080887395 (electronic bk.) 0080887392 (electronic bk.) 128179340X 9781281793409
122258:126279 Elsevier Science & Technology http://www.sciencedirect.com
Neural networks (Computer science)--Mathematics.
Neurale netwerken.
R�eseaux neuronaux (Informatique)--Math�ematiques.
Neural networks (Computer science)--Mathematics.
COMPUTERS--Enterprise Applications--Business Intelligence Tools.
COMPUTERS--Intelligence (AI) & Semantics.
Artificial intelligence Mathematical models
Electronic books.
QA76.87 / .M38 1993eb
006.3
Includes bibliographical references.
Front Cover; Mathematical Approaches to Neural Networks, Volume 51; Copyright Page; Preface; Table of Contents; Chapter 1. Control Theory Approach; Chapter 2. Computational Learning Theory for Artificial Neural Networks; Chapter 3. Time-summating Network Approach; Chapter 4. The Numerical Analysis Approach; Chapter 5. Self-organising Neural Networks for Stable Control of Autonomous Behavior in a Changing World; Chapter 6. On-line Learning Processes in Artificial Neural Networks; Chapter 7. Multilayer Functionals; Chapter 8. Neural Networks: The Spin Glass Approach
Use copy
The subject of Neural Networks is being seen to be coming of age, after its initial inception 50 years ago in the seminal work of McCulloch and Pitts. It is proving to be valuable in a wide range of academic disciplines and in important applications in industrial and business tasks. The progress being made in each approach is considerable. Nevertheless, both stand in need of a theoretical framework of explanation to underpin their usage and to allow the progress being made to be put on a firmer footing. This book aims to strengthen the foundations in its presentation of mathematical approaches to neural networks. It is through these that a suitable explanatory framework is expected to be found. The approaches span a broad range, from single neuron details to numerical analysis, functional analysis and dynamical systems theory. Each of these avenues provides its own insights into the way neural networks can be understood, both for artificial ones and simplified simulations. As a whole, the publication underlines the importance of the ever-deepening mathematical understanding of neural networks.
Electronic reproduction.
[S.l.] :
HathiTrust Digital Library,
2010.
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.
http://purl.oclc.org/DLF/benchrepro0212
9780080887395 (electronic bk.) 0080887392 (electronic bk.) 128179340X 9781281793409
122258:126279 Elsevier Science & Technology http://www.sciencedirect.com
Neural networks (Computer science)--Mathematics.
Neurale netwerken.
R�eseaux neuronaux (Informatique)--Math�ematiques.
Neural networks (Computer science)--Mathematics.
COMPUTERS--Enterprise Applications--Business Intelligence Tools.
COMPUTERS--Intelligence (AI) & Semantics.
Artificial intelligence Mathematical models
Electronic books.
QA76.87 / .M38 1993eb
006.3
