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 62.
Derivation of an empirical model of such a system is unlikely to be successful if at least a sizeable subset of the explanatory variables is not considered ...
rations, ranging from the design of machines to do various things considered to be intelligent, to machines which could provide insight into the mental ...
An elementary feedforward neural network can therefore be considered as a structure with n neurons or nodes receiving inputs (x), and m nodes producing ...
These algorithms are considered in more detail below. 1.5. TRAINING RULES As was mentioned above, neural networks are essentially data driven devices that ...
... di(t)}x(t) (1.14) Different learning rules can be distinguished on the basis of their different learning signals, as considered in more detail below.
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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