Optimization Of Double Layer Grid Structures Using FEM, SPSA And Neural Networks
Optimization of square-on-square double layer grids is beneficial for design purpose. For this purpose, use of a gradient based optimization algorithm incorporating stochastic feature called static perturbation stochastic approximation (SPSA) has not investigated. A computational procedure for cons...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.usm.my/42441/1/REZA_KAMYAB_MOGHADAS.pdf |
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| Summary: | Optimization of square-on-square double layer grids is beneficial for design purpose. For this purpose, use of a gradient based optimization algorithm incorporating
stochastic feature called static perturbation stochastic approximation (SPSA) has not investigated. A computational procedure for constrained optimization of square-onsquare
double layer grids combining FEM, SPSA algorithm and neural network has been formulated. Using the formulated procedures, a total of 208 set of optimization have been carried out on square-on-square double layer grids with different combinations of span L(25m~75m) and height h (0.035L~0.095L). Of the 208 sets of data, 173 and 35 have been used in the training and testing of radial basis
function(RBF) and generalized regression(GR) neural networks for prediction of optimal design and the corresponding maximum deflection of square-on-square
double layer grids with different spans and heights. |
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