A Three-Dimensional Model-Based Inversion Algorithm Using Radial Basis Functions for Microwave Data

2012 ◽  
Vol 60 (7) ◽  
pp. 3361-3372 ◽  
Author(s):  
Maokun Li ◽  
Aria Abubakar ◽  
Tarek M. Habashy
Keyword(s):  
Radial Basis Functions ◽  
Three Dimensional ◽  
Basis Functions ◽  
Dimensional Model ◽  
Inversion Algorithm ◽  
Three Dimensional Model ◽  
Model Based ◽  
Radial Basis ◽  
Microwave Data

scholarly journals A three dimensional model-based simulation of the wrist joint during radioulnar deviation

2015 ◽  
Vol 81 (827) ◽  
pp. 14-00653-14-00653
Author(s):  
Satoshi SHIMAWAKI ◽  
Takushi SHINDO ◽  
Naotaka SAKAI ◽  
Masataka NAKABAYASHI
Keyword(s):  
Wrist Joint ◽  
Three Dimensional ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
Model Based

scholarly journals Three-Dimensional Swirl Flow Velocity-Field Reconstruction Using a Neural Network With Radial Basis Functions

2001 ◽  
Vol 123 (4) ◽  
pp. 920-927 ◽  
Author(s):  
J. Pruvost ◽  
J. Legrand ◽  
P. Legentilhomme
Keyword(s):  
Neural Network ◽  
Velocity Field ◽  
Radial Basis Functions ◽  
Swirling Flow ◽  
Interpolation Method ◽  
Measurement Techniques ◽  
Three Dimensional ◽  
Basis Functions ◽  
Hydrodynamic Characteristics ◽  
Radial Basis

For many studies, knowledge of continuous evolution of hydrodynamic characteristics is useful but generally measurement techniques provide only discrete information. In the case of complex flows, usual numerical interpolating methods appear to be not adapted, as for the free decaying swirling flow presented in this study. The three-dimensional motion involved induces a spatial dependent velocity-field. Thus, the interpolating method has to be three-dimensional and to take into account possible flow nonlinearity, making common methods unsuitable. A different interpolation method is thus proposed, based on a neural network algorithm with Radial Basis Functions.

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