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 92.
2. lappuse
... values. The data are obtained by exposing a chemical sample to an energy source, and recording the resulting absorbance as a continuous trace over a range of wavelengths. Such a trace is consequently digitised at appropriate intervals ...
... values. The data are obtained by exposing a chemical sample to an energy source, and recording the resulting absorbance as a continuous trace over a range of wavelengths. Such a trace is consequently digitised at appropriate intervals ...
7. lappuse
... value or weight, which is an indication of the strength of the connection. The flow of information through the node is ... values, which are the scalar product of the weight and input vectors of the node. The argument of the activation ...
... value or weight, which is an indication of the strength of the connection. The flow of information through the node is ... values, which are the scalar product of the weight and input vectors of the node. The argument of the activation ...
9. lappuse
... value or argument of the i'th neuron in the network is therefore ©i ~ 2}="wijk, for i = 1, 2, ... IIl (1.6) This value is ... values (a process called supervised learning). The optimized weights constitute a distributed internal ...
... value or argument of the i'th neuron in the network is therefore ©i ~ 2}="wijk, for i = 1, 2, ... IIl (1.6) This value is ... values (a process called supervised learning). The optimized weights constitute a distributed internal ...
11. lappuse
... values. The method is elucidated by the example below. Assume the set of training vectors to be x1 = [1, 2, 3 || 1], x2 = [-1, -2, -1 || -1] and x3 = [-3, -1, 0 -1]. The learning rate is assumed to be 3 = 0.1, and the initial weight ...
... values. The method is elucidated by the example below. Assume the set of training vectors to be x1 = [1, 2, 3 || 1], x2 = [-1, -2, -1 || -1] and x3 = [-3, -1, 0 -1]. The learning rate is assumed to be 3 = 0.1, and the initial weight ...
15. lappuse
... values. More specifically, the cost function can be expressed in terms of the weight matrix of the network (which ... value and the activation of the node, that is r = d - w;"x (1.24) and Awi = B[d, Training Rules 15.
... values. More specifically, the cost function can be expressed in terms of the weight matrix of the network (which ... value and the activation of the node, that is r = d - w;"x (1.24) and Awi = B[d, Training Rules 15.
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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