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 60.
v. lappuse
... similar way (and are indeed). Since the book is aimed at the practicing metallurgist or a process engineer in the first place, a considerable part of it is of necessity devoted to the basic theory, which is introduced as briefly as ...
... similar way (and are indeed). Since the book is aimed at the practicing metallurgist or a process engineer in the first place, a considerable part of it is of necessity devoted to the basic theory, which is introduced as briefly as ...
2. lappuse
... Similar problems are encountered with the measurement of acoustic signals, such as may be the case in on-line monitoring of process equipment (Zeng and Forssberg, 1992), potentiometric measurements to monitor corrosion, or image ...
... Similar problems are encountered with the measurement of acoustic signals, such as may be the case in on-line monitoring of process equipment (Zeng and Forssberg, 1992), potentiometric measurements to monitor corrosion, or image ...
8. lappuse
... similar 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 ...
... similar 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 ...
9. lappuse
... similar to that for the neuron model, except that a double index i,j notation is used to describe the destination (first subscript) and source (second subscript) nodes of the weights. The activation value or argument of the i'th neuron ...
... similar to that for the neuron model, except that a double index i,j notation is used to describe the destination (first subscript) and source (second subscript) nodes of the weights. The activation value or argument of the i'th neuron ...
16. lappuse
... similar to the Hebbian rule discussed below (with a binary activation function and z = d.), except that it is a supervised learning rule. Similar to Hebbian learning, the initial weight vector should also be zero. 1.5.2. Unsupervised ...
... similar to the Hebbian rule discussed below (with a binary activation function and z = d.), except that it is a supervised learning rule. Similar to Hebbian learning, the initial weight vector should also be zero. 1.5.2. Unsupervised ...
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 Networks and ... C. Aldrich Ierobežota priekšskatīšana - 2002 |
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
Populāri fragmenti
335. lappuse - The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network.
360. lappuse - Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces.
338. lappuse - Shavlik, JW (1994). Using sampling and queries to extract rules from trained neural networks.
341. lappuse - A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, DS, Leen, TK (eds.) Advances in Neural Information Processing Systems, vol.
363. lappuse - The GENITOR Algorithm and Selective Pressure: Why Rank-Based Allocation of Reproductive Trials is Best, in Proc.
127. lappuse - Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant...
348. lappuse - Kramer, MA (1991). Nonlinear Principal Component Analysis Using Autoassociative Neural Networks.
190. lappuse - Zi£j[(i-Hx)G-Hy){f(iJ,d,a)/CTxCTy}] (5.14) where \\.x and a^ are respectively the mean and standard deviation of the row sums of the matrix, and \iy and ay are the mean and standard deviation of the column sums.
80. lappuse - The sum of the variances of all n principal components is equal to the sum of the variances of the original variables.
21. lappuse - Each process element in the Kohonen layer measures the Euclidean distance of its weights to the input values (exemplars) fed to the layer. For example, if the input data consist of M-dimensional vectors of the form x = {x\,xi, . . .xM}, then each Kohonen element will have M weight values, which can be denoted by w, = {Wi\,Wii,...WiM}.