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.

Table of Contents

1. Introduction to neural networks. 1.1 Background. 1.2 Artificial neural networks from an engineering perspective. 1.3 Brief history of neural networks. 1.4 Structures of neural networks. 1.5 Training rules. 1.6 Neural, network models. 1.7 Neural networks and statistical models. 1.8 Applications in the process industries. 2. Training of neural networks. 2.1 Gradient descent methods. 2.2 Conjugate gradients. 2.3 Newton's method and quasi-Newton method. 2.4 Levenberg-Marquardt algorithm. 2.5 Stochastic methods. 2.6 Regularization and pruning of neural network model. 2.7 Pruning algorithms for neural networks. 2.8 Constructive algorithms for neural network. 3. Latent variable methods. 3.1 Basics of latent structure analysis. 3.2 Principal component analysis. 3.3 Nonlinear approaches to latent variable extraction. 3.4 Principal component analysis with neural networks. 3.5 Example 2: feature extraction from digitised images of industrial flotation froths with auto associative neural networks. 3.6 Alternative approaches to nonlinear principal component analysis. 3.7 Example 1: low-dimensional reconstruction of data with non linear principal component methods. 3.8 Partial least squares (PLS) models. 3.9 Multivariate statistical process control. 4. Regression models. 4.1 Theoretical background to model development. 4.2 Regression and correlation. 4.3 Multicollinearity. 4.4 Outliers and influential observations. 4.5 Robust regression models. 4.6 Dummy variable regression. 4.7 Ridge regression. <


No. of pages:
© 2002
Elsevier Science
Print ISBN:
Electronic ISBN:

About the editor

C. Aldrich

Affiliations and Expertise

Department of Chemical Engineering, Faculty of Engineering, University of Stellenbosch, Banhoek Road, Stellenbosch 7600, South Africa


@from:Henrik Saxen, Åbo Akademi, Finland @qu:What our readers say about this title ...a welcome contribution to the field of exploratory data analysis.
Henrik Saxen, Åbo Akademi, Finland @source: @from:Derick Moolman, CSense Systems, European Office @qu:What our readers say about this title a must for the process engineer with a role and interest in process troubleshooting. ...The author is clearly a world-class expert in the field. ...I strongly recommend this book for engineering students, process engineers, production and quality managers, consulting and technology companies in the process industry (not only in metallurgy). ...Our company, that provides practical troubleshooting software products and services to end-users in the process industry, makes use of this book, because it is the best for our requirements.
Derick Moolman, CSense Systems, European Office @source: