4 April 1997 Direct, inverse, and combined problems in complex engineered system modeling by artificial neural networks
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This paper summarizes theoretical findings and applications of artificial neural networks to modeling of complex engineered system response in the abnormal environments. The thermal fire impact on the industrial container for waste and fissile materials was investigated using model and experimental data. Solutions for the direct problem show that the generalization properties of neural network based model are significantly better than those for standard interpolation methods. Minimal amount of data required for good prediction of system response is estimated in computer experiments with MLP network. It was shown that Kohonen's self-organizing map with counterpropagation may also estimate local accuracy of regularized solution for inverse and combined problems. Feature space regions of partial correctness of the inverse model can be automatically extracted using adaptive clustering. Practical findings include time strategy recommendations for fire-safe services when industrial or transport accidents occur.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Serge A. Terekhoff, Serge A. Terekhoff, } "Direct, inverse, and combined problems in complex engineered system modeling by artificial neural networks", Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271527; https://doi.org/10.1117/12.271527


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