Exploratory Analysis of Metallurgical Process Data with Neural Networks and Related Methods

Pirmais vāks
Elsevier, 2002. gada 19. apr. - 386 lappuses
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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Saturs

CHAPTER 1 INTRODUCTION TO NEURAL NETWORKS
1
CHAPTER 2 TRAINING OF NEURAL NETWORKS
50
CHAPTER 3 LATENT VARIABLE METHODS
74
CHAPTER 4 REGRESSION MODELS
112
CHAPTER 5 TOPOGRAPHICAL MAPPINGS WITH NEURAL NETWORKS
172
CHAPTER 6 CLUSTER ANALYSIS
199
CHAPTER 7 EXTRACTION OF RULES FROM DATA WITH NEURAL NETWORKS
228
CHAPTER 8 INTRODUCTION TO THE MODELLING OF DYNAMIC SYSTEMSCHAPTER
262
DYNAMIC SYSTEMS ANALYSIS AND MODELLING
285
CHAPTER 10 EMBEDDING OF MULTIVARIATE DYNAMIC PROCESS SYSTEMS
299
CHAPTER 11 FROM EXPLORATORY DATA ANALYSIS TO DECISION SUPPORT AND PROCESS CONTROL
313
REFERENCES
333
INDEX
366
DATA FILES
370
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Populāri fragmenti

335. lappuse - The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network.
360. lappuse - Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces.
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