Usually, excess mean square error (MSE) and convergence speed are two criterions used to evaluate performance.
剩余均方误差(MSE)和收敛速度是衡量均衡算法性能的两个常用标准。
The experimental results demonstrate that the MSE (mean square error) reduced by this proposed approach decreases 30% - 60% than that by Laplacian pyramid and discrete wavelet transform approaches.
实验表明该方法融合结果的均方误差比拉普拉斯金字塔算法和小波变换方法降低约30%- 60%。
Taking the channel estimation mean square error (MSE) as the performance evaluation indicator, the thesis verifies the efficiency of the proposed scheme from the theoretical aspect.
并且以信道估计均方误差(MSE)作为评估标准，从理论上证明了所提算法的有效性。
The validity and feasibility of the algorithm is tested through a simulation experiment and two appraisal criterions-mean square error (MSE) and peak value signal to noise ratio (PSNR).
通过仿真实验结果及均方误差(MSE)和峰值信噪比(PSNR)两种评价准则说明了该算法的有效性和可行性。
After speed of statistical convergence and MSE (Mean Square Error) of these algorithms are compared, Bayes Generalization algorithm is assessed to be the most appropriate.
在比较收敛速度和均方差后，确定选用贝叶斯正则算法建立模型。
After speed of statistical convergence and MSE (Mean Square Error) of these algorithms are compared, Bayes Generalization algorithm is assessed to be the most appropriate.
在比较收敛速度和均方差后，确定选用贝叶斯正则算法建立模型。
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