Exploratory Analysis of Metallurgical Process Data with Neural Networks and Related Methods
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.
1.5. rezultāts no 96.
Generalization implies that the neural network can interpolate sensibly at points not contained in its training set, as indicated in Figure 1.3.
The solid and empty circles indicate training and test data respectively. The problem is then to relate the matrix Y to some set of functions Y = f(X) of ...
When all the network weights are adjusted for the k'th exemplar (i.e. for all i and j) as indicated above, it is referred to as per sample training or ...
The mapping between points in the p-dimensional latent space and the m-dimensional data space is represented by a function y(x,\W), as indicated in Figure ...
... i.e. w;" - c | <|w; - c. l. This divides the vector space into a socalled Voronoi (or Dirichlet) tesselation, as indicated in Figure 1.9.
Lietotāju komentāri - Rakstīt atsauksmi
CHAPTER 3 LATENT VARIABLE METHODS
CHAPTER 4 REGRESSION MODELS
CHAPTER 5 TOPOGRAPHICAL MAPPINGS WITH NEURAL NETWORKS
CHAPTER 6 CLUSTER ANALYSIS
CHAPTER 7 EXTRACTION OF RULES FROM DATA WITH NEURAL NETWORKS
CHAPTER 8 INTRODUCTION TO THE MODELLING OF DYNAMIC SYSTEMSCHAPTER
DYNAMIC SYSTEMS ANALYSIS AND MODELLING
CHAPTER 10 EMBEDDING OF MULTIVARIATE DYNAMIC PROCESS SYSTEMS
CHAPTER 11 FROM EXPLORATORY DATA ANALYSIS TO DECISION SUPPORT AND PROCESS CONTROL
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Exploratory Analysis of Metallurgical Process Data with Neural Networks and ...
Ierobežota priekšskatīšana - 2002