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 46.
In general terms neural networks are therefore simply computers or computational structures consisting of large numbers of primitive process units connected ...
Models of single neurons Each node model consists of a processing element with a set of input connections, as well as a single output connection, ...
Models of neural network structures Neural networks consist of interconnections of nodes, such as the ones described above. These processing nodes are ...
... of its weights to the input values (exemplars) presented to the nodes. For example, if the input data consist of m-dimensional vectors Training Rules 17.
For example, if the input data consist of m-dimensional vectors of the form x = [x1, x2, ... xm] ,. then. each. node. will. have.
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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