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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。
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。
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。
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 prediction error percentage of the 52000t all-purpose cargo ship compared with the model test result is 21.8%, 300000t VLCC compared with the measured value is merely 3.64%. 用该公式预报的5.;2万吨多用途货船的脉动压力值与空泡水筒中的实验值相比误差为21
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。
Wavelet networks are used to model the prediction error to compensate for the predictive output. 为了提高输出预报精度，采用小波网络对预报误差进行预测，作为输出预报的补偿。
On the basis of hypothesis of first ordered linear error this paper derived the kinematics posture error model of end operator. 摘要基于一阶线性误差假设推导了空间机器人末端操作器运动学位姿误差模型。
Meanwhile, GM (1,1) is of higher precision and less prediction errors than Metabolizing Model's. 而在此实例中,GM(1,1)模型比新陈代谢的预测精度更高、预测误差更小。
Objective To introduce the basic theory of linear measurement error model and apply it to medical research. 目的介绍线性测量误差模型的基本理论,探讨它在医学研究领域中的应用。
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。
SINS error model and the Kalman filter are used for realizing precise alignment of the SINS under vibration state. 仿真结果表明，补偿摆动误差后，基本消除车体摆动干扰，得到与静基座粗对准相当的结果；
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。
The error model of system is set up, the precision and velocity of the aligning scheme is further researched. 建立了系统的误差模型，深入研究了该对准方案的精度和速度，并与传统的卡尔曼滤波器进行仿真比较。
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。
Example verified that linear measurement error model can fit data with great measurement error and can give fine results. 线性测量误差模型能够在很大程度上解决由测量误差带来的问题,针对具体问题拟合恰当的线性测量误差模型,可以得到合理的结果。
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；
The empirical results show that Fractionally integrated GARCH with GED error model performs the best in estimating five percent VaR. 实证结果表明在估计95%25置信度下的VaR值时基于GED分布的FIGARCH(1,d,1)模型表现最佳。
The result shows that a second order ARMA model gives the best fit to the data in terms of sum of squares of prediction errors and in terms of the whiteness of the residual. 两种算法都进行了仿真，结果表明：从预测偏差的平方和及残数的空白度来看，二阶的ARMA模型会产生最佳的数据适应度。