1) elastic variable structure discrete dynamic bayesian networks
弹性变结构离散动态贝叶斯网络
2) variable structure discrete dynamic bayesian networks
变结构离散动态贝叶斯网络
3) discrete dynamic Bayesian network
离散动态贝叶斯网络
1.
Structure varied discrete dynamic Bayesian network and its inference algorithm;
变结构离散动态贝叶斯网络及其推理算法
2.
At the very beginning, a new inferring method was proposed, which synthesized different observable parameters of target characters based on discrete dynamic Bayesian network model.
提出基于离散动态贝叶斯网络模型,对若干可观测的目标特征参数进行综合推理。
4) dynamic Bayesian networks
动态贝叶斯网络
1.
Dynamic fault tree analysis based on dynamic Bayesian networks;
基于动态贝叶斯网络的动态故障树分析
2.
Geometric-pattern dynamic Bayesian networks reasoning gene regulatory networks;
几何模式动态贝叶斯网络推理基因调控网络
3.
The prime task and the content in this article can be concluded as follows:(1) The basic framework of information fusion based on dynamic Bayesian networks was discussed.
鉴于贝叶斯网络,尤其是动态贝叶斯网络在处理不确定信息方面的优点,本文重点研究了基于动态贝叶斯网络的目标毁伤等级评估方法及其应用。
5) dynamic Bayesian network
动态贝叶斯网络
1.
Study on path planning of UCAV based on dynamic Bayesian network;
基于动态贝叶斯网络的无人机路径规划研究
2.
In this paper,linking Kalman filter theory,we bring out a new moving target state prediction model based on dynamic Bayesian network and used to shot moving targets.
提出基于动态贝叶斯网络推理的火炮攻击敌纵深运动目标状态估计模型。
3.
In solving the dynamic uncertainty problem based on dynamic Bayesian networks(DBNs),the efficiency of processing is decided by the inference algorithm.
基于动态贝叶斯网络处理动态不确定性问题的过程中推理是非常重要的,而推理算法的优劣决定推理的执行效率;文章在分析联合树性质的基础上提出一种较简单的3/2片联合树算法,该算法不需要限制消去顺序且只作一次扩展;讨论了算法的复杂度,并作了试验比较。
6) dynamic bayesian network(DBN)
动态贝叶斯网络
1.
In view of tracking difficulty of multiple view angles about multi-actors from monocular video,a tracking method based on Dynamic Bayesian Network(DBN) is proposed which operates on monocular gray-scale video imagery.
该文提出一种基于动态贝叶斯网络的分类特征联合建模的跟踪方法,将视频中基于时空的运动特征和轮廓特征相复合,采用先粗后精的方法解决由于视觉角度不同而造成的跟踪困难,实现同一场景中多视角下的多人跟踪。
2.
To accurately capture the variations of real speech spectra,two single stream Dynamic Bayesian Network(DBN) models based on context-dependent triphone:SS-DBN-TRI model and SS-DBN-TRI-CON model,are proposed for continuous speech recognition.
考虑连续语音中的协同发音问题,提出基于词内扩展的单流上下文相关三音素动态贝叶斯网络(SS-DBN-TRI)模型和词间扩展的单流上下文相关三音素DBN(SS-DBN-TRI-CON)模型。
3.
A semi-supervised active learning algorithm is proposed to overcome the difficulties in getting sufficient labeled training sample data set during the process of building dynamic Bayesian network(DBN) classification model.
本文提出一种基于半监督主动学习的算法,用于解决在建立动态贝叶斯网络(DBN)分类模型时遇到的难以获得大量带有类标注的样本数据集的问题。
补充资料：离散时间周期序列的离散傅里叶级数表示
(1)
式中χ((n))N为一离散时间周期序列，其周期为N点，即
式中r为任意整数。X((k))N为频域周期序列，其周期亦为N点，即X(k)=X(k+lN)，式中l为任意整数。
从式(1)可导出已知X((k))N求χ((n))N的关系
(2)
式(1)和式(2)称为离散傅里叶级数对。
当离散时间周期序列整体向左移位m时,移位后的序列为χ((n+m))N，如果χ((n))N的离散傅里叶级数(DFS)表示为,则χ((n+m))N的DFS表示为
式中χ((n))N为一离散时间周期序列，其周期为N点，即
式中r为任意整数。X((k))N为频域周期序列，其周期亦为N点，即X(k)=X(k+lN)，式中l为任意整数。
从式(1)可导出已知X((k))N求χ((n))N的关系
(2)
式(1)和式(2)称为离散傅里叶级数对。
当离散时间周期序列整体向左移位m时,移位后的序列为χ((n+m))N，如果χ((n))N的离散傅里叶级数(DFS)表示为,则χ((n+m))N的DFS表示为
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条