Exploratory analysis of Metallurgical process data with neural networks and related methods

C. Aldrich (Eds.)9780444503121, 0444503129

This volume is concerned with the analysis and interpretation of multivariate measurements commonly found in the mineral and metallurgical industries, with the emphasis on the use of neural networks.
The book is primarily aimed at the practicing metallurgist or process engineer, and a considerable part of it is of necessity devoted to the basic theory which is introduced as briefly as possible within the large scope of the field. Also, although the book focuses on neural networks, they cannot be divorced from their statistical framework and this is discussed in length. The book is therefore a blend of basic theory and some of the most recent advances in the practical application of neural networks.

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Table of contents :
Content:
Preface
Page v
Chris Aldrich

Chapter 1 Introduction to neural networks Original Research Article
Pages 1-49

Chapter 2 Training of neural networks Original Research Article
Pages 50-73

Chapter 3 Latent variable methods Original Research Article
Pages 74-111

Chapter 4 Regression Models Original Research Article
Pages 112-171

Chapter 5 Topographical mappings with neural networks Original Research Article
Pages 172-198

Chapter 6 Cluster analysis Original Research Article
Pages 199-227

Chapter 7 Extraction of rules from data with neural networks Original Research Article
Pages 228-261

Chapter 8 Introduction to the modelling of dynamic systems Original Research Article
Pages 262-284

Chapter 9 Case studies: Dynamic systems analysis and modelling Original Research Article
Pages 285-298
C. Aldrich, J.P. Barnard

Chapter 10 Embedding of multivariate dynamic process systems Original Research Article
Pages 299-312
C. Aldrich, J.P. Barnard

Chapter 11 From exploratory data analysis to decision support and process control Original Research Article
Pages 313-332

References
Pages 333-365

Index
Pages 366-369

Appendix: Data files
Page 370