rmse

The process of which is to solve the integer programming problem with the objective function that RMSE is minimal .

选择最优参数的过程即是对以均方根误差最小为目标的整数规划问题的求解过程。

The experiment results show that , the method significantly reduces the RMSE value , improves the PSNR value , and makes that the fused image has good effect .

实验结果表明，与其他多聚焦图像融合方法相比，该方法显著减少了均方根误差，提高了峰值信噪比，使得融合图像效果较好。

And RMSE is being used to evaluate the model accuracy . ( 3 ) Adjacency effect .

并采用RMSE对模型精度进行了评估。（3）邻近效应的去除。

Otherwise , the N-SPMP algorithm has low RMSE still in small array .

从存在阵列误差对高分辨算法的仿真结果可以看出，当阵列误差较小时，N-SPMP算法仍有较好的估计性能。

The results show that this algorithm improves the visual effect of the fused image and decreases the RMSE value .

实验结果表明，与其他基于多分辨率分析的多聚焦图像融合算法相比，该算法显著减小了融合图像的RMSE值，提高了融合图像的视觉效果。

Specific modeling steps and prediction accuracy measures ( RMSE , MAPE ) of the corrosion prediction model were given .

给出了腐蚀预测模型的具体建模步骤和预测精度评价指标（RMSE、MAPE）。

2-D rigid image transformation model and parameter estimation using the rule of least root mean square error ( RMSE ) are discussed .

讨论了图像变换模型及基于最小均方根误差准则的图像变换参数估计方法。

The RMSE for single boll weight and abscission ratio were 0.15 g and 8.15 % .

单铃重的RMSE为015g；脱落率的RMSE为815%。

However , the RMSE values were low , hence good . Finally , a comparison was done between the Regression model and ANFIS model .

然而，RMSE值很低，因此效果很好。最后，比较了回归模型和ANFIS模型。

Using the independent checking samples , the results were evaluated , and the root mean square error ( RMSE ) was 16.526 m3 / hm2 .

利用独立的检验样本对估测结果进行评价，均方根误差（RMSE）为16.526m3/hm2，与实际情况基本相符。

Comparing the RMSE and MSE values , through the analysis of the instance shows that this improved method is better than the past , the method of that method is effective .

通过实例分析比较RMSE和MSE值，表明本文的改进方法要优于过去已有的方法，说明方法是有效的。

With respect to the ranges of RMSE , the minimum RMSE can be achieved by the simulated annealing sampling model and stratified random sampling model .

RMSE极差最小为模拟退火和分层随机采样模式。

And the RMSE for single ridge stage and anther separation stage , which were important to apical development for barley , were 1.8 d and 3.2 d.

对于比较重要的单棱期和药隔形成期，RMSE分别为1.8和3.2d，均没有超过4d。模型表现出较强的机理性和实用性。

The RMSE for stamen and pistil initiation stage were 3.7 d , which was bigger than that of other stages .

对雌雄蕊分化期的模拟误差较大，RMSE为3.7d；

Optimization result of inductors with a variety of geometry sizes shows that our approach is effective to achieve accurate models with RMSE below 5 % in a wide frequency range .

优化结果表明，针对不同几何参数的螺旋电感，该优化方法都能得到误差在5%以内的宽频带电感模型。

In order to further reduce the limit RMSE , in this thesis , we proposed a new learning system based on multi-bidirectional extreme learning machine called parent-offspring progressive learning machine .

本文利用上述所提出的双端极限学习机构造一个多神经网络学习系统以降低神经网络极限误差，并将其命名为父代子代渐进学习机。

The simulated results showed that the piecewise linear model performed better than the other two methods with a ME ( Model Efficiency ) by0.99 and a less RMSE ( Root Mean Square Error ) .

对比结果表明：分段线性订正模型的模拟效果优于其他两种方法，其模型效率达到0.99，均方差也明显偏小；

Selecting the biggest scale , it is not the smaller de-noising result better for RMSE of de-noising , it is very close with he measured data , Mean square error limit is mentioned .

对于最大尺度的选取，并不是去噪后的均方误差RMSE越小则去噪效果越好，只能表示与实测数据很接近。

In detection , the Zernike vector of the selected area is first extracted , and the root-mean-square-error ( RMSE ) is employed to decide the presence of the watermark .

水印检测时，计算局部区域的Zernike矩，并与已知Zernike矩矢量计算均方误差根（RMSE）判断水印存在与否。

This paper points out that the commonly used indicator RMSE tends to serious imputation errors by a specific example . As it is , we propose a new evaluation method called goodness to overcome the defect .

通过具体的实例分析，本论文指出常用的填充效果指标RMSE容易偏向严重的填充误差，并提出一种新的goodness评价方式。

Then randomly selected detection point on the created grid DEM and calculated RMSE to assessing the accuracy . And then compared the contours map extractied from grid DEM to the original contour map .

然后在生成的栅格DEM上随机选取检测点，计算中误差进行精度评定，并用栅格DEM提取同一等高距的等高线图与原等高线图作比较。

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 ) are41.8 and55.7 , respectively .

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。

The smaller the smoothing parameter , the more iterations are necessary to reach the minimum RMSE ; 4.The RMSE of the best estimator with different smoothing parameters are nearly the same , but visual effect is different .

较小的平滑参数需要更多的迭代次数；4、不同平滑参数的最佳估计的误差很接近，但视觉效果不同。

The adaptive lifting scheme performs better than the interpolating wavelet and Haar wavelet , and can reduce the root mean square error ( RMSE ) and local point error ( LPE ) without the redundant storing space of the sign .

与非自适应的插值小波以及常用的Haar小波相比，这种自适应的提升格式降低了均方根误差（RMSE）和单点误差（LPE）。

Secondly , the problem of threshold determination in wavelet threshold de-noising of the test signal is discussed , the de-noising effect of different threshold is compared and a threshold calculation method based on the intersection point of SNR and RMSE proportion curves is proposed .

然后讨论测试信号小波阈值消噪法中阈值的确定问题，对比了不同阈值的消噪效果，并提出了SNR和RMSE比例曲线交点的阈值计算方法。

Through simulation and analyse of the error between the simulated value and the measured value according to the root mean squares error ( RMSE ) standard , it was shown that the dynamic model can provide reliable estimates of both temperature and humidity in the greenhouse .

根据均方根误差RMSE标准分析了模拟值与测量值的误差，结果分析表明，建立的温室小气候动态模型能可靠地估计温室内空气的温湿度值。

The RMSE values are about 5 , and the disease index errors are 0.08-8.33 between model forecasting and field surveying . The results showed that the models could be also used in practical forecasting for disease index of cucumber downy mildew in the plastic greenhouse tunnel .

利用筛选出的预测模型对上海地区霜霉病的发生情况进行了预测模拟，RMSE值在5左右，表明模型可对塑料大棚中黄瓜霜霉病的流行状况进行定量预测。

For the out-sample forecasting , we use the mean absolute error ( MAE ) and root mean square error ( RMSE ) to measure and compare , the results show that : the prediction model we use established by the four factors is reasonable and effective .

对于样本外的预测，我们分别使用平均绝对误差（MAE）和均方根误差（RMSE）进行度量比较，结果表明：我们使用四个因子所建立的预测模型是合理、有效的。

The model was validated using the data sets of NDVI in jointing , heading and filling stage , with the root mean square error ( RMSE ) of 4.57 % , 4.2 % and 3.84 % for grain starch content in winter wheat , respectively .

结果表明，模型的监测值与实测值较为一致，利用拔节期、抽穗期、灌浆期遥感影像NDVI和气候环境数据预测籽粒淀粉含量的RMSE值分别为4.57%，4.2%和3.84%。

Statistics show that the power function model fitted the best , being 0.9990 in average determination coefficient , higher than the other three ; but was lower in average relative error and root mean square error ( RMSE ) than the other three ( ANOVA , P0.001 ) .

统计检验显示，幂函数模型的光谱拟合效果最好，平均决定系数为0.9990，大于其他3中模型，而平均相对误差、均方根误差则小于其他3种模型。