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probability error
相关语句
  概率误差
     Then the author present two reduction methods,probability er ror reduction and average probability error reduction in an incomplete informati on system. It is given that reduction algorithm correspondingly.
     由此提出了不完备信息系统的2种约简方法即概率误差分布约简和平均概率误差约简,以及相应的约简算法.
短句来源
     Aiming to some projectiles of trench mortar,the probability error of the experiment without controlling flight is about 1%. Monte Carlo target practice was used in the same calculating condition.
     针对某型迫弹无控飞行时的试验中间概率误差约为1%,利用蒙特卡洛打靶法,在相同的计算条件下,若末段采用脉冲控制,仿真计算得到其圆概率误差小于等于4 m;
短句来源
  “probability error”译为未确定词的双语例句
     The numbers were fit for the formula of numbers of samples in Statistics. The accuracy and verify of vegetation types distribution in the map was analyzed by building a Probability Error Matrix (PEM) and through the variance analysis. The results indicated that the overall accuracy (OA) of the vegetation map was 84.7%.
     混淆矩阵计算得出植被图总的判对精度即整体精度OA为 84 7% ,利用计算成数方差检验 ,结果表明大部分类型为 90 %以上 .
短句来源
     The calculating circle probability error is not more than 4 m under the condition of unpulse control,and is not more than 2.5 m under the condition of the rudder control.
     若末段采用舵机控制,其圆概率误差小于等于2.5 m.
短句来源
  相似匹配句对
     Error Probability of Differential MSK
     差分MSK的误码性能
短句来源
     PROBABILITY
     概率论
短句来源
     Estimation and Test of Circular Error Probability
     圆概率偏差的测定与检验
短句来源
     On Estimation of the Probability P(X>E(X))
     概率P(X>E(X))的估计问题
短句来源
     Error and uncertainty
     误差与不确定度
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  probability error
It is shown that the capability of the noisy channel to ensure state estimation with a bounded in probability error is identical to its capability to transmit information with as small probability of error as desired.
      
The second step is to decide which voxels in the smooth map are activated, for a prespecified type I probability error a.
      
The corresponding 63% probability error ellipses are shown centred on the tip of each velocity arrow.
      
To do this, some considerations have to be made on probability error we aim at and on a priori knowledge.
      
Because of the finiteness of the standard deviation the probability error is always finite.
      
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On the basis of the acquisition of velocity field in a cold model which simulates the W-shape flame boiler installed in Shang'an, Heibei province, a Monte-Carlo-Method math ematical simulation has been developed to simulate the three dimensional heat transfer in this boiler with plate superheaters inside. Both furnace and wall temperature profiles are obtained. The calculating result shows agreement with the fact. Meanwhile, a number mesh method is firstly used in this paper to solve the three dimensional heat...

On the basis of the acquisition of velocity field in a cold model which simulates the W-shape flame boiler installed in Shang'an, Heibei province, a Monte-Carlo-Method math ematical simulation has been developed to simulate the three dimensional heat transfer in this boiler with plate superheaters inside. Both furnace and wall temperature profiles are obtained. The calculating result shows agreement with the fact. Meanwhile, a number mesh method is firstly used in this paper to solve the three dimensional heat transfer in large scale furnace. This method saves computer time and avoids probability error.

本文以河北上安电厂W型火焰煤粉锅炉为对象,在冷态试验台测试了炉内空气动力场的基础上,利用Monte-Carlo方法对带屏式过热器的W型火焰煤粉锅炉炉膛内部进行了三维传热数值模拟,获得了炉内及壁面的温度分布,所得结果与锅炉的实际运行情况基本吻合。同时,本文还首次将数论网格方法应用于大型燃烧室的三维传热计算,这一方法在缩短计算时间、消除概率偏差等方面有着明显的优越性。

In order to manipulate image information optimally, the vision system must quantitatively take the measurement uncertainties into account, especially the digital quantization errors. In this paper a normal probability error model of point features in a general motion vision system is presented. We provide a novel representation, which describes uncertainty with the expects of the feature coordinates as well as the inverse of the covariance matrix. 2D image points uncertainties can also be represented...

In order to manipulate image information optimally, the vision system must quantitatively take the measurement uncertainties into account, especially the digital quantization errors. In this paper a normal probability error model of point features in a general motion vision system is presented. We provide a novel representation, which describes uncertainty with the expects of the feature coordinates as well as the inverse of the covariance matrix. 2D image points uncertainties can also be represented by an extension of this unified 3D form. Explicit formulas and experimental results for both error generation and evolution are presented.

为了充分利用图像信息,运动视觉系统需要定量处理测量误差,特别是像素量化效应导致的不确定性.在提出一般运动视觉系统点特征误差的正态概率模型的基础上,用特征坐标的期望值和协方差矩阵之逆来描述位置的不确定性,其扩展形式建立了二维图像点和三维特征点位置不确定性的统一描述.同时也具体给出了误差形成、传播和估计的计算公式和实验结果.

As a knowledge representation framework and a kind of probability inference engine, Bayesian networks are widely used in applications for reasoning and decision making with inherent uncertainty. Since the exact algorithms of probability inference in Bayesian networks is NP-hard, as the topology of the network becomes more dense, the run-time complexity of probabilistic inference increases dramatically and real-time decision making eventually becomes prohibitive, so many approximate algorithms based on simulation...

As a knowledge representation framework and a kind of probability inference engine, Bayesian networks are widely used in applications for reasoning and decision making with inherent uncertainty. Since the exact algorithms of probability inference in Bayesian networks is NP-hard, as the topology of the network becomes more dense, the run-time complexity of probabilistic inference increases dramatically and real-time decision making eventually becomes prohibitive, so many approximate algorithms based on simulation or model simplification are proposed. The method discussed in this paper is based on the model simplification of arc removal. In this method, a subset of arcs are selected and removed, which simplifies the network structure and we obtain an approximate network, then any probability inference algorithm can be applied to this approximate network to get a solution within the error bound we predefined. By using the Kullback-Leibler information divergence as the measure of the difference between two probability distributions, this paper discusses the multiple arc removal problem in the gen eral case and presents the optimal parameters for the approximate network. Final ly, a heuristic algorithm is provided which searches a set of arcs to be removed under the upper bound on the probability error allowed.

弧的删除是一种对 Bayes网络模型进行近似的方法 .文中以 Kullback- L eibler偏差作为近似网络和原网络概率分布误差的测度 ,给出了近似网络在此测度意义下的最优参数 .同时 ,也给出了通过对原网络删除多条弧进行近似的启发式算法 ,当给定一个误差上界时 ,可以使用此算法寻找满足误差要求的近似网络

 
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