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At the same time, linear regression, nonlinear regression and radial basis function (RBF) neural network models are set up to evaluate weld quality between the selected parameters and tensile-shear strength.
      
For the RBF neural network model, which is more effective for monitoring weld quality than the others, the average error validated is 2.88% and the maximal error validated is under 10%.
      
The provision of residents of the European North of Russia with vitamin B2 (riboflavin (RBF)) and the activity of the erythrocytic RBF-dependent enzyme glutathione reductase (EC 1.6.4.2) were studied.
      
in residents of the European North of juvenile and senile age The provision with RBF showed a tendency toward a decrease as compared to other age groups of the population.
      
The parameters of provision with RBF were correlated significantly with the level of physical activity, alcohol status, and season of the year.
      
Analysis of the haplotype frequency distribution patterns constructed from the data on the DYS287and RBF5polymorphisms revealed substantial differences between Udmurts and other populations.
      
Haplotype TAT-C (RBF5 locus) was observed in all populations, except Shorians, with the frequencies varying from 5.4% in Altai-Kizhi to 18.8% in Khakassians.
      
Crystal structures of Rb5F4(HF2) · 2H2O and RbF · H2O
      
The crystal structures of the Rb5F4(HF2) · 2H2O and RbF · H2O compounds are determined.
      
Efficient gradient descent method of RBF neural entworks with adaptive learning rate
      
Detailed deduction of the algorithm applied to RBF networks is given.
      
Immune RBF network and its application in the modulation-style recognition of radar signals
      
Based on Immune Programming(IP), a novel Radial Basis Function (RBF) network designing method is proposed.
      
The application of the RBF network trained with the algorithm in the modulation-style recognition of radar signals demonstrates that the network has a fast convergence speed with good performances.
      
The kernel function of HVDM-RBF (Heterogeneous Value Difference Metric Radial Basis Function), based on the heterogeneous value difference metric of heterogeneous datasets, is constructed for the heterogeneous network data.
      
HVDM-RBF and one-against-one method are applied to build MSVM.
      
The data for training and testing the radial basic function (RBF) neural-networks model of greenhouse were collected in a 1028 m2 multi-span glasshouse.
      
Radial basis function (RBF) is adopted to map facial texture from the frontal image to the reconstructed face geometry.
      
First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control.
      
RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980's.
      
 

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