• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.1
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• pp.137-146
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• 2002

The predictions of the loading characteristics was performed by the neural networks which use the results through structural analysis. The momentum backperpagtion which can be modified the teaming rate and momentum coefficient, was developed. Input patterns of the neural networks are the 9 strains which positioned at the side of the shell and output layers is the loading characteristics. Hidden layers were increased from 1 layers to 3 layers. Developed program which were trained by 9 strains predict the loading characteristics under 0.5%. Inverse engineering can be applicable to the composite laminated cylindrical shells with developed neural networks.

• Composites Research
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• v.14 no.5
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• pp.26-37
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• 2001

After impact analysis of the composite cylindrical shells was performed. obtained outputs at 9 equally divided points of the shell were used as input patterns of the neural networks. Identification of impact loading characteristics was predicted simultaneously. Momentum backpropagation algorithm of neural networks which can modify the momentum coefficient and learning rate was developed and applied to identify the loading characteristics. Hidden layers of the backpropagation increased from 1 layer to 3 layers and trained the loading characteristics. Developed program with variable learning rate was converged close to real load characteristics under 1% error. Inverse engineering which identify the impact loading characteristics can be applicable to the composite laminated cylindrical shells with developed neural networks.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.11 no.9
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• pp.475-485
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• 2001

A study on the structural analysis of the composite laminated cylindrical shell which has simply supported boundary conditions at both ends, was performed. The results were used into the neural networks. Neural networks identify the load characteristics of the composite shells. Momentum Backpropagation which the learning rate can be varied was developed. Input patterns consist of strains at 9 side points which is divided equally. Output layers are the load characteristics. Developed program was used for the training. The training with variable learning rate was converged close to real oad characteristics. Inverse engineering can be applicable to the composite laminated cylindrical shells with developed neural networks.