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 46.
3. lappuse
... computation itself. Computational systems are limited by the system overheads required to supply the energy and to get rid of the heat, i.e. the boxes, the heaters, the fans, the connectors, the circuit boards, and the other ...
... computation itself. Computational systems are limited by the system overheads required to supply the energy and to get rid of the heat, i.e. the boxes, the heaters, the fans, the connectors, the circuit boards, and the other ...
4. lappuse
... computational efficiency, the human brain also excels in many other respects. It differs significantly from current ... computation can therefore be seen as an alternative to the usual one based on a programmed instruction sequence ...
... computational efficiency, the human brain also excels in many other respects. It differs significantly from current ... computation can therefore be seen as an alternative to the usual one based on a programmed instruction sequence ...
5. lappuse
... computational methods it will also find a solid niche in this field. A closer look at the historic development of neural networks will underpin the analogous paths of these two branches of artificial intelligence. 1.3. BRIEF HISTORY OF ...
... computational methods it will also find a solid niche in this field. A closer look at the historic development of neural networks will underpin the analogous paths of these two branches of artificial intelligence. 1.3. BRIEF HISTORY OF ...
6. lappuse
... computational structures consisting of large numbers of primitive process units connected on a massively parallel scale. These units, nodes or artificial neurons are relatively simple devices by themselves, and it is only through the ...
... computational structures consisting of large numbers of primitive process units connected on a massively parallel scale. These units, nodes or artificial neurons are relatively simple devices by themselves, and it is only through the ...
8. lappuse
... computational characteristics. A distinction is made between input, hidden and output layers, depending on their relation to the information environment of the neural network. The nodes in a particular layer are linked to other nodes in ...
... computational characteristics. A distinction is made between input, hidden and output layers, depending on their relation to the information environment of the neural network. The nodes in a particular layer are linked to other nodes in ...
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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