动态贝叶斯网络

本文提出了基于动态贝叶斯网络的战场信息预测与评估。

In this paper forecasting and evaluating are based on DBN .

动态贝叶斯网络在传感器逻辑结构动态优化中的应用

The Application of DBN for Dynamic Optimization Design on Sensor Logic Structure

基于动态贝叶斯网络的多Agent交互的模型表示

Model Representing of Multi-agent Interactions Based on Dynamic Bayesian Networks

动态贝叶斯网络的一种基于BK的粒子滤波推理算法

Particle Filter for Dynamic Bayesian Networks Inference Based on BK Algorithm

同时，基于贝叶斯网络（BayesianNetworks，BN）的风险评价模型和动态贝叶斯网络（DynamicBayesianNetworks，DBN）建立了在投项目风险预警模型，以实现对在投项目的风险控制；

At the same time build the Risk evaluation model based on Bayesian Networks ( BN ) and investment risk presentiment model based on Dynamic Bayesian Networks ( DBN ), which can realize risk control of ongoing investment .

用Matlab软件对流域网络模型进行构造和仿真。预报结果表明，动态贝叶斯网络应用于水文预报具有初步成效，它是建立流域水文预报模型的一种直观的、有效的方法。

Finally , we realize and simulate this model with Matlab language , and the results indicate that hydrologic forecast have the initial success based on Dynamic Bayesian Networks , and it is a visualized and effective method in hydrologic forecast of river basin .

该方法根据多源先验信息的特点将其转换为不同分布的网络结构先验概率，并与基因芯片时序表达数据相结合，通过马尔可夫链蒙特卡罗法（MarkovchainMonteCarlo，MCMC）学习N阶动态贝叶斯网络的结构。

In order to com-bining with time series microarray data , this method transforms multiple independent sources of prior knowledge into different prior probability distributions according to their characteristic , and uses Markov Chain Monte Carlo ( MCMC ) algorithm to learn the network structure of N-DBN .

以北京东部地区1994年、2001年和2003年5月LANDSATTM遥感数据为例，介绍了利用动态贝叶斯网络进行多时相遥感变化检测的基本过程。

Taking the Landsat TM remote sensing data of eastern Beijing area acquired in May of 1994,2001 and 2003 as an example , we introduce in detail the method to do multi-temporal remote sensing direct change detection using Dynamic Bayesian Networks .

针对传统自适应粒子滤波（APF）对于动态贝叶斯网络推理中高维的问题，提出动态贝叶斯网络一种自适应的局部抽样粒子滤波算法（LSAPF）。

This paper proposed a local sample adaptive particle filtering ( LSAPF ) to resolve the high dimension problem of traditional adaptive particle filter for dynamic Bayesian networks inference .

动态贝叶斯网络（DBN），以其扩展性和对时间序列的强大描述、推导和学习能力，逐渐被应用于连续语音识别中。

Dynamic Bayesian Network ( DBN ), because of extensibility , powerful description , inference and learning abilities for the time series , being used in the speech recognition .

对于建立动态贝叶斯网络（DBN）分类模型时，带有类标注样本数据集获得困难的问题，提出一种基于EM和分类损失的半监督主动DBN学习算法。

A semi-supervised active DBN learning algorithm based on EM and classification loss is set forth for building Dynamic Bayesian Networks ( DBN ) classifier when it is difficult to get sufficient labeled training data .

利用动态贝叶斯网络（DBNs）在处理不同时相遥感数据时可以一次性输入多个时间段的数据，同时完成分类和建立输出类别之间的关联。

Utilizing Dynamic Bayesian Networks ( DBNs ) to deal with multi-temporal remote sensing data , the multi-temporal data of different time can be input simultaneously , and the classification and the acquirement of relationships between the output types can be finished simultaneously .

但是，该模型要求原始信息必须完整、无时延。（5）提出了基于模糊动态贝叶斯网络（FDBN）的能耗自预测模型。

However , the original information used by this model must be complete and punctual . ( 5 ) An energy consumption self-prediction model based on Fuzzy Dynamic Bayesian Network ( FDBN ) is proposed .

结合发音特征的动态贝叶斯网络语音识别模型

Novel Articulatory Feature based Dynamic Bayesian Network model for speech recognition

基于动态贝叶斯网络的公路交通事件算法

Detection Methods for Expressway Traffic Events Based on Dynamic Bayesian Networks

多时相遥感变化检测的动态贝叶斯网络研究

Study on Dynamic Bayesian Networks for Multi-temporal Remote Sensing Change Detection

基于动态贝叶斯网络的无人机路径规划研究

Study on path planning of UCAV based on dynamic Bayesian network

基于离散动态贝叶斯网络的辐射源目标识别研究

Research on Radiant Point Identification Based on Discrete Dynamic Bayesian Network

基于离散模糊动态贝叶斯网络的空战态势评估及仿真

Modeling Air Combat Situation Assessment by Using Fuzzy Dynamic Bayesian Network

动态贝叶斯网络的近似推理算法研究

The Research on Approximate Inference Algorithm for Dynamic Bayesian Networks

研究了基于扩展动态贝叶斯网络的过程控制系统建模方法。

The process control systems are modeled using extended dynamic Bayesian networks .

变结构离散动态贝叶斯网络及其推理算法

Structure varied discrete dynamic Bayesian network and its inference algorithm

在非时齐马氏决策过程中的动态贝叶斯网络研究

Research on Dynamic Bayesian Networks in Non Time Homogenous Markov Decision Process

一种基于半监督主动学习的动态贝叶斯网络算法

A Dynamic Bayesian Network Algorithm Based on Semi-Supervised Active Learning

动态贝叶斯网络及其在说话人识别中的应用

Dynamic Bayesian Network and Its Application to Speaker Recognition

离散动态贝叶斯网络的直接计算推理算法

Direct calculation inference algorithm for discrete dynamic bayesian network

利用动态贝叶斯网络进行多时相遥感变化检测

Multi-temporal Remote Sensing Change Detection Using Dynamic Bayesian Networks

连续动态贝叶斯网络实现多传感器融合跟踪

Multisensor Fusion Tracking Using Continuous Dynamic Bayesian Network

基于多层动态贝叶斯网络的人的行为多尺度分析及识别方法

Approach to Human Activity Multi-scale Analysis and Recognition Based on Multi-layer Dynamic Bayesian Network

本文采用基因表达时序数据，利用动态贝叶斯网络方法重构多时延基因调控网络，并利用遗传算法对网络结构进行优化，以提高准确率。

This paper adopts gene expression sequence and use dynamic Bayesian networks to reconstruct the gene regulatory networks .