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 44.
The output of the neuron can be expressed as follows. z= f(X="wix), or z = f(w'x) (1.1) where w is the weight vector of the neural node, defined as ...
can be treated as follows, if the behaviour of the process is characterized by data of the following form Y1, Y12 . Yip Y2.1 y22 .
... di(t)]x(t) (1.13) The parameter B determines the learning rate, so that the weight vector is updated at discrete time steps as follows w;(t+1) = wi(t) + ...
... supervisory procedure takes place as follows Awi = -ÉVE (1.20) Or Aw; - B[d - f(w'x)|f(w's)x (1.21) for a single 12 Introduction to Neural Networks.
... as follows Awp = 0(x-wn), or AWip = 0(xj-Wipold) (1.33) where a is an appropriate learning coefficient which decreases with time (typically starting at ...
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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