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人工神經網絡預測純金屬的表面張力

Estimating Surface Tension of Pure Metals by Neural Network

  • 摘要: 建立了以純金屬原子半徑、熔點、沸點和原子化焓預測表面張力的人工神經網絡模型.訓練后的神經網絡能較好的擬會實驗數據.對40種金屬的表面張力進行回想和預測結果與實驗值的偏差在可接受范圍內,表明人工神經網絡在純金屬表面張力預測方面有一定的前景.

     

    Abstract: An artificial neural network was established to forecast the surface tension of pure metal from the experimental data of atomic radius, melting point, boiling point and atomization enthalpies. The trained network can represent the relahonship between the input factors and output factor (surface tension).The associated and forecast data for more than 40 pure metals are acceptable considering the deviation of the experimental dara for surface tension, which shows a good prospect of artificial neural network in the prediction of surface tension of pure metals.

     

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