Exploratory Analysis of Metallurgical Process Data with Neural Networks and Related Methods
Elsevier, 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.
1.5. rezultāts no 51.
... hard-limiting version (unipolar sign functions, or unipolar binary functions) the same as for the bipolar activations functions (equations 1.3-1.4).
(1.7) Neural networks with multiple layers are simply formed by cascading the single-layer networks represented by equations 1.6-1.7.
z) =%[d - f(w'x)]? (1.18) Calculation of the gradient vector of the squared error in equation, with regard to wi, gives VE = -[d - f(w'z)|f(w'x)x (1.19) The ...
0), that is Aw;(t) = CAwji(t-1)+ to;(t)y(t) (1.23) Equation (1.23) is known as the generalized delta rule, since it includes the delta rule (0. = 0).
... d) Correlation learning rule The correlation rule is obtained by substituting r = di in the general learning rule (equation 1.12), so that Awi = Bd;x, ...
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CHAPTER 3 LATENT VARIABLE METHODS
CHAPTER 4 REGRESSION MODELS
CHAPTER 5 TOPOGRAPHICAL MAPPINGS WITH NEURAL NETWORKS
CHAPTER 6 CLUSTER ANALYSIS
CHAPTER 7 EXTRACTION OF RULES FROM DATA WITH NEURAL NETWORKS
CHAPTER 8 INTRODUCTION TO THE MODELLING OF DYNAMIC SYSTEMSCHAPTER
DYNAMIC SYSTEMS ANALYSIS AND MODELLING
CHAPTER 10 EMBEDDING OF MULTIVARIATE DYNAMIC PROCESS SYSTEMS
CHAPTER 11 FROM EXPLORATORY DATA ANALYSIS TO DECISION SUPPORT AND PROCESS CONTROL
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Exploratory Analysis of Metallurgical Process Data with Neural Networks and ...
Ierobežota priekšskatīšana - 2002