A novel electrocardiogram compression algorithm based on minimum root-mean-square error prediction utilizing the correlation of adjacent QRS wave is proposed.
本文提出了一种利用相邻QRS波的相关性进行最小均方差预测的心电图压缩算法。
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.
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。
In addition, with the use of the parameters such as entropy, average of image, root mean square error, peak signal-to-noise ratio, the performance of the fusion scheme is evaluated and analyzed.
此外，利用熵、图像均值、均方根误差、峰值信噪比等参量对该融合方法的融合性能进行评价与分析。
The results show that the root mean square error(RMSE) of the control points obtained by the workflow is lower than 0.5 pixels.
结果表明，使用该方法选取的控制点均方根误差（RMSE）可以控制在0.5个像素以内。
Coefficient determination, absolute bias, relative absolute bias, root mean square error and relative root mean square error were employed to evaluate the precision of different model systems.
采用确定系数、绝对误差、相对绝对误差、均方根误差、相对均方根误差等模型评价指标对不同模型系统的精度进行比较分析。
The methods of the evaluation of image fusion, which includes: entropy, cross entropy, mutual information, mean square error, root mean square error, peak-to-peak signal-to-noise ratio were presented.
介绍了图像融合的各种评价方法，包括：熵、交叉熵、交互信息量、均方误差、均方根误差、峰值信噪比。
By selecting sub-ensemble with smaller error, the root mean square error of forecast is reduced by over 10%.
采用最优集合子集预报方式时的臭氧预报均方根误差比原确定性预报低了10%以上。
The correlation coefficients (r) and root mean square error of prediction (RMSEP) were used as the model evaluation indices.
以预测集的预测相关系数(r)，预测标准偏差(RMSEP)作为模型评价指标。
The correlation coefficients (r) and root mean square error of prediction (RMSEP) were used as the model evaluation indices.
以预测集的预测相关系数(r)，预测标准偏差(RMSEP)作为模型评价指标。
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