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 73.
... they are considered first. a) Basic structure As mentioned previously, ... or weighted connections (adjustable numeric values), as shown in Figure 1.5.
... functions can be seen as a small computational overhead (Bishop, 1995). ... to one or more two-dimensional Kohonen layers, as shown in Figure 1.6.
Consequently, there is a well-defined objective function given by the log ... a socalled Voronoi (or Dirichlet) tesselation, as indicated in Figure 1.9.
As shown in Figure 1.12, the neural network version of this Bayesian classifier consists of an input layer, a normalizing layer (which normalizes the ...
... hidden (pattern) layers, as well as output layers, as shown in Figure 1.14. ... the hidden node activation functions can be described by z (x;,0,0) ...
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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