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 52.
v. lappuse
... techniques differ from univariate or bivariate techniques, in that they focus on the covariances or correlations of three or more variables, instead of the means and variances of single variables, or the pairwise relationship between ...
... techniques differ from univariate or bivariate techniques, in that they focus on the covariances or correlations of three or more variables, instead of the means and variances of single variables, or the pairwise relationship between ...
2. lappuse
... techniques for the efficient exploration of data have been around for decades. However, it is only now with the growing availability of processing power that these techniques have become sophisticated instruments in the hands of ...
... techniques for the efficient exploration of data have been around for decades. However, it is only now with the growing availability of processing power that these techniques have become sophisticated instruments in the hands of ...
3. lappuse
... techniques encompass heuristic programming, goal-based reasoning, parsing and causal analysis and are efficient systematic search procedures, capable of the manipulation and rearrangement of elements of complex systems or the ...
... techniques encompass heuristic programming, goal-based reasoning, parsing and causal analysis and are efficient systematic search procedures, capable of the manipulation and rearrangement of elements of complex systems or the ...
4. lappuse
... techniques inspired by the study of the human brain. Although the brain is a very complex organ that is still largely an enigma, despite considerable advances in the neurosciences, it is clear that it operates in a massively parallel ...
... techniques inspired by the study of the human brain. Although the brain is a very complex organ that is still largely an enigma, despite considerable advances in the neurosciences, it is clear that it operates in a massively parallel ...
10. lappuse
... techniques based on the use of neural networks, is that a priori assumptions with regard to the functional relationship between X and Y are not required. The network learns this relationship instead, on the basis of examples of related ...
... techniques based on the use of neural networks, is that a priori assumptions with regard to the functional relationship between X and Y are not required. The network learns this relationship instead, on the basis of examples of related ...
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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