结果表明，前馈控制模型具有良好的预测能力，模型最大和平均预测误差分别小于8%和5%。
The results show that the feed-forward control model has good predication ability. Maximal and average prediction errors of the model are less than 8% and 5%, respectively.
结果表明，小波神经网络方法比传统BP网络方法平均预测误差减小了1%，收敛速度加快了3倍。
It is shown that, compared with the results from traditional BP neural network, the average error rate is reduced by 1% and the convergence speed raised by 3 times.
用标准预测误差(%SEP)，平均预测误差(MPE)和平均相对误差(MRE)来评价其预测能力。
The standard error of prediction (% SEP), the mean prediction error (MPE) and the mean relative error (MRE) were utilized to evaluate the prediction ability of the BP-ANN.
由该模型对95个聚合物的折光率进行预测，平均相对误差为0.959％。
The average prediction error by this model is 0.959% for the refractive index of 95 amorphous homopolymers.
利用BP神经网络模型实现了对造纸废水处理过程的预测，平均相对误差为19%，表明网络泛化能力不是很好。
The effluent treatment process was predicted with this BP neural network model with the average relative error of 19%, which indicates that the generalization power of the network is not so desirable.
数值实验显示，虽然策略库比较简单，但其预测的平均相对误差仅为1.73%。
The numerical experiment results show that the mean forecast relative error is 1.73% even with a simple strategies library.
预测与实钻结果吻合较好，实钻误差平均为11%。
The prediction value tallied with the actual drilling result, and the average error was 11%.
预测的热负荷、热效率及温度的平均相对误差均小于10%。
The mean relative deviations of the predicted heat load, heat efficiency and temperature are less than 10%.
利用二次移动平均模型，引入均方拟合误差最小的原理确定出时段数n，对国民经济总收入、人口数量等项目进行了预测。
The total income of national economy and the amount of population are predicted with second-degree moving model and with the principle of least mean square fit error determining designated value n.
将此动力学模型预测固定床反应结果，计算值与实测值相对平均误差小于20%。
The model was used to predict the reaction results of fixed-bed reactor and the average deviations of the calculated values from experimental data were less than 20%.
结果表明：硬度预测值与实验值相对误差的绝对值的平均值为5.90%，附着力预测准确率为100%，耐冲击性预测准确率为100%。
The results show that the average absolute value of relative errors between predictive and measured values of hardness is 5.90%, the prediction accuracies of adhesion and impact resistance are 100%.
预测350种各类物质13117个温度点的汽化热，与文献常用值相比较的总绝对平均误差为2.8％。
The equation was tested for 350 pure substances of 13117 temperature points, the absolute average deviation was 2.8%.
实验结果表明，模型预测结果的平均相对误差为10.316%，相对标准差为12.895%，满足工程实际要求。
Experiment shows that the average relative error of predicted results is 10.316% and the relative standard error is 12.895%, thus it satisfying requirements of the engineering.
驱油效率实际值与预测值平均相对误差8.7%，精度满足生产要求。
The relative average error between predicted oil displacement efficiency and actual value is 8.7%, the accuracy meets demand of production.
利用网络预测误差的相对平均值对神经网络的泛化能力进行了定量的分析。
Making use of the relative average of network prediction uncertainty, the quantitative analysis is carried out for the ability of generalization.
模拟的结果显示ANN模型比线性回归模型有更好的预测能力，预测的平均相对误差：ANN模型为14.9%，线性回归模型为25.8%。
Simulation results showed that the ANN model gave better predictions than the regressive model. The average relative error of ANN was 14.9% and that of linear regression was 25.8%.
顾名思义，平均绝对误差是绝对误差的平均值，其中fi是预测值而yi是实际值。
As the name suggests, the mean absolute error is an average of the absolute errors, where is the prediction and the true value.
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%.
仿真与实验结果表明：该模型预测和控制板形精度的平均误差绝对值均在5％之内，具有较好的效果，可应用于板形在线预测与控制。
The experimental results show that the absolute value of mean error of such prediction and control precision is within 5% , which gets good effect on sheet shape control. The BP network m…
从P次幂误差的概念出发，提出了广义加权算术平均组合预测法新的预测方法优超和冗余度的定义。
New dominant forecasting method and redundant measure are defined for combination forecasting method with generalized weight arithmetic average, based on error of power of p.
利用GA - BP神经网络模型对气井产量进行了拟合和预测，拟合的平均相对误差为5.1%，表明新模型适用于洛带气田的产量递减预测。
GA-BP neural network model is applied in matching and predicting the production of the gas Wells with 5.1% of the average relative error. It proves th...
对预测结果的分析表明，多点外推法不仅减少了平均误差，也使最大误差降低，能有效提高预测精度。
Practical results show that the proposed method lowers both average error and maximum error and improves forecasting precision.
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102种脂肪胺的计算结果表明，沸点预测值与实验值的一致性令人满意，平均误差0．422％。
The calculated results show that the Predicted values of boiling points are in good agreement with the experimental data, and the mean relative deviation is 0.422% for 102 alkyl …
对102种脂肪胺的计算结果表明，沸点预测值与实验值的一致性令人满意，平均误差0．422％。
The calculated results show that the Predicted values of boiling points are in good agreement with the experimental data, and the mean relative deviation is 0.422% for 102 alkyl …
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