scholarly journals The dependence identification neural network construction algorithm

1996 ◽  
Vol 7 (1) ◽  
pp. 3-15 ◽  
Author(s):  
J.O. Moody ◽  
P.J. Antsaklis
Keyword(s):  
Neural Network ◽  
Network Construction ◽  
Construction Algorithm

Artificial Neural Network and Genetic Algorithms for Discontinuously Reinforced Aluminum Composites Frictional Behaviour Prediction

2005 ◽  
Author(s):  
Ming Qiu ◽  
Yong-Zhen Zhang ◽  
Jun Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Working Conditions ◽  
Composite Component ◽  
Network Construction ◽  
Predictive Quality ◽  
Modeling Data ◽  
Model Of Friction ◽  
Frictional Behaviour

By using genetic algorithms and radius basis function (GARBF) neural network, the predicting model of friction coefficient has been established based on a measured database with five sliding velocities of 40, 55, 70, 85, 100 m/s and four different normal pressures of 0.1333, 0.4667, 0.60 and 0.7333 MPa. The modeling results confirm the feasibility of the GARBF network and its good correlation with the experimental results. The predictive quality of the GARBF network can be further improved by enlarging the training datasets and by optimizing the network construction. A well-trained GARBF modeling is expected to be very helpful for selecting composite component under different working conditions, and for predicting tribological properties. Finally, by using GARBF modeling data to predict analysis, the results show that the friction coefficients of these composites were increased with the increase in material thermal capability at some region.

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