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 84.
... AND PRUNING OF NEURAL NETWORK MODELS ..................................., 62 2.6.1 Weight decay.............. 62 2.6.2. Removal of weights.
Referred to as the Snark, the device consisted of 300 vacuum tubes and 40 variable resistors, which represented the weights of the network.
Each of these connections is characterized by a numerical value or weight, which is an indication of the strength of the connection.
is fixed, the parameters (weights) of the network have to be determined. This is done by training (optimization) of the weight matrix of the neural network.
The learning signal r is generally a function of the weight vector, w; e 9t", the input x e 93." and a target signal, di e 93, where applicable, ...
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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