Industrial Applications of Neural Networks

Project ANNIE Handbook
Mitarbeit:Croall, Ian F.; Mason, John P.

• Broschiertes Buch

Produktbeschreibung

Das Buch gibt einen vollständigen Überblick über die Einsatzmöglichkeiten neuronaler Netze für industrielle Anwendungen. Es beschreibt die Ergebnisse des ESPRIT-Projekts ANNIE, das von zehn europäischen Partnern aus Industrie und Forschung von 1988 bis 1991 durchgeführt wurde.

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Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Research Reports Esprit 1
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-540-55875-0
• 1992.
• Seitenzahl: 308
• Erscheinungstermin: 10. November 1992
• Englisch
• Abmessung: 242mm x 170mm x 17mm
• Gewicht: 460g
• ISBN-13: 9783540558750
• ISBN-10: 3540558756
• Artikelnr.: 24951882

Inhaltsangabe

1 Introduction.
1.1 Purpose of the handbook.
1.2 Origins of the ANNIE project.
1.3 The ANNIE team.
1.4 Overall objectives of the ANNIE project.
1.5 Applications selected for demonstration of neural network capability.
1.6 Relationship to ESPRIT aims and objectives.
1.7 Layout of the handbook.
2 An Overview of Neural Networks.
2.1 The neural network model.
2.2 Principal features.
2.3 Neural networks used in ANNIE.
3 Implementations of Neural Networks.
3.1 Sequential implementation.
3.2 Examples of implementations of neural networks.
3.3 Parallel implementation.
3.4 Discussion.
3.5 Hardware.
3.6 Floating point systems.
3.7 New processors and components.
3.8 Systolic computation.
3.9 Summary of architectural features.
3.10 Benchmarking.
3.11 Software.
3.12 Environments developed within ANNIE.
3.13 Dedicated neural network hardware.
4 Pattern Recognition.
4.1 Introduction.
4.2 Learning mechanisms and evaluation criteria.
4.3 Generic problems identified by the partners.
4.4 Supervised learning on generic datasets.
4.5 Unsupervised learning.
4.6 Applications of neural networks to pattern recognition in acoustic emission.
4.7 Proof testing of pressure vessels.
4.8 Detection and characterisation of defects in welds from ultrasonic testing.
4.9 ALOC defect detection.
4.10 Solder joints inspection with neural networks from 3D laser scanning.
4.11 Conclusions.
5 Control Applications.
5.1 Introduction.
5.2 Overview on control technology.
5.3 Use of neural networks for control purposes.
5.4 Lernfahrzeug system (NeVIS).
5.5 NeVIS IV.
5.6 Methodology.
5.7 Identification of a moving robot.
6 Optimisation.
6.1 Introduction.
6.2 Conventional methods in combinatorial optimisation.
6.3 Linear programming.
6.4 Integer linear programming.
6.5 Heuristics.
6.6 Neural network methods in combinatorial optimisation.
6.7 The crew scheduling problem.
6.8 A specific airline case.
6.9 The pairing generator.
6.10 Conventional methods for set covering problems.
6.11 The neural network approach.
6.12 Improving the performance of the network.
7 Methodology.
7.1 Introduction.
7.2 Conventional and neural network approaches.
7.3 Implementing solutions.
7.4 ANNIE applications.
7.5 Discussion.
Appendix 1: Partners in the ANNIE Consortium and Project Staff.
Appendix 2: Networks Used in the Project.
A2.1 Introduction.
A2.2 Associative networks.
A2.3 Linear associative networks.
A2.4 Hopfield networks.
A2.5 Bidirectional associative memories.
A2.6 The Boltzmann machine.
A2.7 Error feedback networks.
A2.8 Error feedback learning.
A2.9 The back
propagation algorithm.
A2.10 Self
organising networks.
A2.11 Further studies.
Appendix 3: ANNIE Benchmark Code.
A3.1 Introduction.
A3.2 Interpretation of benchmarks.
A3.3 Some results.
A3.4 Test Code.
Appendix 4: Some Suppliers of Network Simulators.