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 94.
One such example pertains to spectroscopic data, where observations comprise a function, rather than a few discrete values. The data are obtained by ...
Each of these connections is characterized by a numerical value or weight, ... defined on the set of activation values, which are the scalar product of the ...
IIl (1.6) This value is subsequently transformed by the activation function ... sets of output data or target values (a process called supervised learning).
In unsupervised learning, the response of a target value to guide learning is not available. This can be expressed by a generalized learning rule (Amari, ...
The weights of the network can assume any initial values. The method is elucidated by the example below. Assume the set of training vectors to be x1 = [1, ...
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
Citi izdevumi - Skatīt visu
Exploratory Analysis of Metallurgical Process Data with Neural Networks and ...
Ierobežota priekšskatīšana - 2002