A virtual-sample technology based artificial-neural-network for a complex data analysis in a glass-ceramic system

다국어 초록 (Multilingual Abstract)

Artificial neural network has becoming a mainstream technology in the domain of complex materials data analysis. Based on
a slag glass-ceramic system we brought forward a virtual sample technology to increase the training samples by fluctuating
the content of main compositions in a proper small amplitude. Simulation results proved that a good virtual sample set can
not only improve the network’s prediction ability considerably, but can also suppress the “overtraining” phenomenon. Therefore
a virtual sample improved neural network model can learn the relationship from a small size experimental data set and give
an accurate and stable prediction for the test samples. This is more helpful to the material data analysis and can facilitate the
design and development for new materials.

다국어 초록 (Multilingual Abstract)

Artificial neural network has becoming a mainstream technology in the domain of complex materials data analysis. Based on
a slag glass-ceramic system we brought forward a virtual sample technology to increase the training samples by fluctuating
the content of main compositions in a proper small amplitude. Simulation results proved that a good virtual sample set can
not only improve the network’s prediction ability considerably, but can also suppress the “overtraining” phenomenon. Therefore
a virtual sample improved neural network model can learn the relationship from a small size experimental data set and give
an accurate and stable prediction for the test samples. This is more helpful to the material data analysis and can facilitate the
design and development for new materials.

참고문헌 (Reference)

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