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.

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;

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%.

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.

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.

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.

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.

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.