对250个分子298K时原子化能计算的平均绝对误差(Mean Absolute Deviation，MAD)和最大误差(Maximum Deviation，MD)分别为2.079kcal\mol和11.358...
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结果显示，该模型预测效果明显优于传统的线性自回归预测模型，各月平均的平均绝对误差（MAE）和均方误差（RMSE）达到41.8和55.7。
Results show that the RBFNN is obviously superior to the traditional linear model, and its MAE (mean absolute error) and RMSE (root mean square error) are 41.8 and 55.7, respectively.
对现场收集的数据进行仿真学习，结果表明，该预测模型收敛速度快，具有较高的预测精度，平均绝对误差可达到0.002 7%。
Some data were chosen to train the network model. The results show that the convergence rate was faster, the model had higher accuracy, the average absolute error can reach 0.002 7%.
CT法测得的平均密度与煤油法测定值相比，平均绝对误差不到1 . 2 %。
Comparing mean density measured by CT with kerosene mass method, mean error is less 1.2%.
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