Exploratory Analysis of Metallurgical Process Data with Neural Networks and Related MethodsElsevier, 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. The book is primarily aimed at the practicing metallurgist or process engineer, and a considerable part of it is of necessity devoted to the basic theory which is introduced as briefly as possible within the large scope of the field. Also, although the book focuses on neural networks, they cannot be divorced from their statistical framework and this is discussed in length. The book is therefore a blend of basic theory and some of the most recent advances in the practical application of neural networks. |
No grāmatas satura
1.–5. rezultāts no 30.
2. lappuse
... approximately 200, 700 and 1700 such variables for each chemical sample (Krzanowski and Marriot, 1994). In these cases the number of variables usually exceed the number of samples by far. Similar problems are encountered with the ...
... approximately 200, 700 and 1700 such variables for each chemical sample (Krzanowski and Marriot, 1994). In these cases the number of variables usually exceed the number of samples by far. Similar problems are encountered with the ...
3. lappuse
... approximately 100 to 1000 times more energy on a box level. Compared to the energy requirements of the human brain, with. 1Electronic Numerical Integrator And Computer, the first general purpose electronic computer built in the Moore ...
... approximately 100 to 1000 times more energy on a box level. Compared to the energy requirements of the human brain, with. 1Electronic Numerical Integrator And Computer, the first general purpose electronic computer built in the Moore ...
4. lappuse
C. Aldrich. Compared to the energy requirements of the human brain, with approximately 10” synapses, each of which receives a nerve pulse roughly 10 times per second, the brain accomplishes approximately 10" complex operations per second ...
C. Aldrich. Compared to the energy requirements of the human brain, with approximately 10” synapses, each of which receives a nerve pulse roughly 10 times per second, the brain accomplishes approximately 10" complex operations per second ...
6. lappuse
... approximately $7 million industry in 1987, to an estimated $120 million industry in 1990. 1.4. STRUCTURES OF NEURAL NETWORKS Although much of the development of neural networks has been inspired by biological neural mechanisms, the link ...
... approximately $7 million industry in 1987, to an estimated $120 million industry in 1990. 1.4. STRUCTURES OF NEURAL NETWORKS Although much of the development of neural networks has been inspired by biological neural mechanisms, the link ...
14. lappuse
... approximately 0.867. For step 2: The activation of the node is w(1)Tx2 = [1.0092, 0.0185, -0.9723][-1, -2, -1]T ... approximately 0.474. The delta rule requires a small learning rate (approximately 0.001 < 3s 0.1), since the weight ...
... approximately 0.867. For step 2: The activation of the node is w(1)Tx2 = [1.0092, 0.0185, -0.9723][-1, -2, -1]T ... approximately 0.474. The delta rule requires a small learning rate (approximately 0.001 < 3s 0.1), since the weight ...
Saturs
1 | |
50 | |
CHAPTER 3 LATENT VARIABLE METHODS | 74 |
CHAPTER 4 REGRESSION MODELS | 112 |
CHAPTER 5 TOPOGRAPHICAL MAPPINGS WITH NEURAL NETWORKS | 172 |
CHAPTER 6 CLUSTER ANALYSIS | 199 |
CHAPTER 7 EXTRACTION OF RULES FROM DATA WITH NEURAL NETWORKS | 228 |
CHAPTER 8 INTRODUCTION TO THE MODELLING OF DYNAMIC SYSTEMSCHAPTER | 262 |
DYNAMIC SYSTEMS ANALYSIS AND MODELLING | 285 |
CHAPTER 10 EMBEDDING OF MULTIVARIATE DYNAMIC PROCESS SYSTEMS | 299 |
CHAPTER 11 FROM EXPLORATORY DATA ANALYSIS TO DECISION SUPPORT AND PROCESS CONTROL | 313 |
REFERENCES | 333 |
INDEX | 366 |
DATA FILES | 370 |
Citi izdevumi - Skatīt visu
Exploratory Analysis of Metallurgical Process Data with Neural ..., 1. sējums Chris Aldrich Priekšskatījums nav pieejams - 2002 |
Bieži izmantoti vārdi un frāzes
activation addition algorithm analysis application approach approximately associated attractor attribute calculated classification cluster coefficients complexity computational considered consists constructed containing continuous correlation curve data set decision defined dependent derived determined dimension direction distance distribution dynamic embedding equation error estimated example exemplars extracted Figure fitted follows fuzzy rules Gaussian given hidden layer indicated individual initial input learning least linear matrix means measure methods mill minimize multivariate neural network nodes noise nonlinear objects observations obtained operator optimal original output parameters pattern performance plant points possible prediction principal component principal component analysis problem projection radial basis function reconstructed region regression represented respectively rules sample scale selected separation shown in Figure similar single space squares statistical step structure Table techniques tree values variables variance vector weight
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