A neural network approach to the scattering from a spherical shell with a circular aperture

2002 ◽  
Author(s):  
A.-K. Hamid ◽  
H. Al Duwaish
Keyword(s):  
Neural Network ◽  
Spherical Shell ◽  
Circular Aperture ◽  
Network Approach ◽  
Neural Network Approach

Scattering from a spherical shell with a circular aperature using neural network approach

1998 ◽  
Vol 76 (1) ◽  
pp. 63-67 ◽  
Author(s):  
A -K Hamid
Keyword(s):  
Neural Network ◽  
Spherical Shell ◽  
Cross Section ◽  
Circular Aperture ◽  
Field Equations ◽  
Neural Network Approach ◽  
Cpu Time ◽  
Backscattering Cross Section ◽  
The Neural Network ◽  
Nonlinear Relation

A neural network is developed for the problem of scattering from a spherical shell with a circular aperture. The neural network is used to predict the backscattering cross section without the need to solve the vector field equations. The neural network is trained to model the nonlinear relation between the electrical radius of the sphere, half aperture angle, and the backscattering cross section using experimental data. This method is proven to be faster than standard methods and consumes less CPU time. Numerical results are presented graphically for backscattering cross section with electrical radii and half aperture angles different from those are used for training. PACS Nos. 41.10H, 41.90

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

A neural network approach to anomaly detection in spectra

1997 ◽  
Author(s):  
Daniel Benzing ◽  
Kevin Whitaker ◽  
Dedra Moore ◽  
Daniel Benzing ◽  
Kevin Whitaker ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Network Approach ◽  
Neural Network Approach

A Neural Network Approach In A Backward Heat Conduction Problem

2016 ◽  
Author(s):  
Fabio Tokio Mikki ◽  
Edison Issamoto ◽  
Jefferson I. da Luz ◽  
Pedro Paulo Balbi de Oliveira ◽  
Haroldo F. Campos-Velho ◽  
...  
Keyword(s):  
Neural Network ◽  
Heat Conduction ◽  
Heat Conduction Problem ◽  
Network Approach ◽  
Neural Network Approach
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