Cover Image
Read Now

Industrial Applications of Neural Networks Project ANNIE Handbook

Neural network technology encompasses a class of methods which attempt to mimic the basic structures used in the brain for information processing. Thetechnology is aimed at problems such as pattern recognition which are difficult for traditional computational methods. Neural networks have potential...

Full description

Bibliographic Details
Other Authors: Croall, Ian F. (Editor), Mason, John P. (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 1992, 1992
Edition:1st ed. 1992
Series:Project ANNIE
Subjects:
Operations Research
Pattern Recognition
Complexity
Operations Research/decision Theory
Planning
Organization
Artificial Intelligence
Computational Complexity
Numerical Analysis
Decision Making
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 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
  • 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 Simulato
  • 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

Similar Items