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 22.
2. lappuse
... operators sometimes having to cope with the unpredictable dynamics of nonlinear process systems. To aggravate the ... operators (such as OR, AND, NOT, etc.) as a basis for fundamental instructions to these machines, the majority of ...
... operators sometimes having to cope with the unpredictable dynamics of nonlinear process systems. To aggravate the ... operators (such as OR, AND, NOT, etc.) as a basis for fundamental instructions to these machines, the majority of ...
48. lappuse
... operators, such as Thyssen and Krupp-Hoesch Stahl in Germany, Rautaruukki in Finland, VOEST-Stahl in Austria and SSAB Oxelösund in Sweden. The early 1990s saw the implementation of a hybrid neuro-fuzzy control system from Pavilion ...
... operators, such as Thyssen and Krupp-Hoesch Stahl in Germany, Rautaruukki in Finland, VOEST-Stahl in Austria and SSAB Oxelösund in Sweden. The early 1990s saw the implementation of a hybrid neuro-fuzzy control system from Pavilion ...
49. lappuse
... operators. Prototypes of these systems are still being evaluated and they cannot be considered fully commercialised yet. Despite these successes, the on-line implementation of neural networks in advanced control systems is not ...
... operators. Prototypes of these systems are still being evaluated and they cannot be considered fully commercialised yet. Despite these successes, the on-line implementation of neural networks in advanced control systems is not ...
60. lappuse
... operators, such as mutation, while promising regions can be explored more intensely through flexible allocation of the individuals of populations to different regions in the search space. Figure 2.6. Encoding of a neural network with 9 ...
... operators, such as mutation, while promising regions can be explored more intensely through flexible allocation of the individuals of populations to different regions in the search space. Figure 2.6. Encoding of a neural network with 9 ...
61. lappuse
... operators, such as crossover and mutation, cannot be used directly. Instead of using standard genetic algorithms in this case, evolutionary programming where the primary search operator is mutation (Saravanan and Fogel, 1995; Sarkar and ...
... operators, such as crossover and mutation, cannot be used directly. Instead of using standard genetic algorithms in this case, evolutionary programming where the primary search operator is mutation (Saravanan and Fogel, 1995; Sarkar and ...
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}.