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.
Author(s): C. Aldrich (Eds.)
Series: Process Metallurgy 12
Edition: 1
Publisher: Elsevier, Academic Press
Year: 2002
Language: English
Pages: 1-370
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
