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- prediction error ratio 预测误差比
- A recursive prediction error algorithm which converges fast is applied to tra. 采用了收敛速度较快的递推预报误差算法训练神经网络。
- In addition, the B3G Test Platform supports the Bits Error Ratio (BER) test. B3G测试平台还提供了误码率测试的功能。
- The paper introduces the grey forecasting model and analy ses its prediction error as well as application in detail. 详细介绍了灰色预测方法并分析了预测误差及其实用价值。
- An MD prediction error coding method is also proposed using low quality macroblock update. 该方案在丢包环境下取得较好的抗丢包性能。
- It is proved to have the same asymptotic statistical properties as the prediction error method(PEM). 证明了该方法与预报误差法具有相同的渐近和统计性能。
- However, adaptive equalization technique can decrease the effect of ISI and noise, reduce the bit error ratio and trace time-changing channel. 而自适应均衡技术可以降低码间干扰和噪声的影响,减少误码,并能够跟踪时变信道,解决时变波形的严重失真问题。
- The forecast accuracy was proved to be satisfactory with an error ratio less than 6%. 检测了模型的预测精度，结果显示误差小于6%25。
- For the EC_1, EC_(50) and equimolar ratio, prediction errors from the INFCIM at the 50% combination effects in all the validation sets were 0.3%, 6% and 0.6%, respectively. 对于EC_1、EC_(50)和等摩尔浓度值混合物而言，INFCIM模型在50%25实验观测的效应下所产生的预测误差分别为0.;3%25、6%25和0
- In the method, the criterion of final prediction error (FPE) is employed to determine the embedding dimension of samples. 该方法应用最终预报误差(FinalPrediction Error,FPE)准则确定样本的嵌入维数。
- Based on pair-wise error probability the upper bound of bit and codeword error ratio are given in this paper. 基于成对错误概率给出了平均误比特率和误码字率的上界。
- Based on the method of minimum prediction error control, a multiple model adaptive controller( MMAC) for discrete time is presented. 基于最小预测误差控制器设计方法,设计离散时间系统多模型自适应控制器,并引入“局部化”方法。
- Article 5 As to a book whose error ratio is below one-ten thousandth, its editing and proofreading is qualified. 第五条差错率不超过万分之一的图书，其编校质量属合格。
- Through comparing their sums of squared error,it was concluded that prediction error algorithm-based OE model has the best precision. 通过误差平方和的比较,确定利用基于输出误差(OE)模型的预报误差法所建立的模型的精度最高。
- Based on the method of minimum prediction error control, a multiple model adaptive controller (MMAC) for discrete time is presented. 基于最小预测误差控制器设计方法,设计离散时间系统多模型自适应控制器,并引入“局部化”方法。
- The influence of power line channel noises on the bit error ratio (BER) performance of the OFDM system is analyzed by simulation. 通过仿真,分析了低压电力线信道上各种噪声对OFDM系统误码率(BER)性能的影响。
- Then the NN model is trained and the average prediction error is 26.46%, which reaches the demand of environmental management. 经过网络训练，预测平均误差为26.;46%25，满足环境管理的精度要求。
- The algorithm prediction error is larger under ionospheric stormy conditions, which is more prominent for the stations in the low latitudes. 此次磁暴期间，算法的精度明显降低，对于低纬地区的影响更为显著；
- RPE (recursive prediction error) algorithm with the advantage of fast convergence is applied to training the recurrent neural network. 采用递推预报误差算法训练神经网络，具有收敛速度快、收敛精度高的特点。
- The simulation results show that the system outperforms fixed modulation FMT system in Bit Error Ratio (BER), and it is a practical bit allocation algorithm. 通过仿真分析表明，与采用固定调制方式的FMT系统相比较，该算法显著提高了系统的误比特率性能，是一种实用的比特分配算法。
