助手标题  
全文文献 工具书 数字 学术定义 翻译助手 学术趋势 更多
查询帮助
意见反馈
   bayesian network 在 电信技术 分类中 的翻译结果: 查询用时:0.199秒
图标索引 在分类学科中查询
所有学科
电信技术
自动化技术
计算机软件及计算机应用
互联网技术
数学
电力工业
宏观经济管理与可持续发展
航空航天科学与工程
工业通用技术及设备
更多类别查询

图标索引 历史查询
 

bayesian network
相关语句
  贝叶斯网络
    A mouth outline feature extraction based on Bayesian Tangent Shape Model(BTSM) and a lip-reading system based on Dynamic Bayesian Network(DBN) is proposed for a talking head in this paper.
    为实现文本/语音驱动的说话人头部动画,提出基于贝叶斯切线形状模型的口形轮廓特征提取方法和基于动态贝叶斯网络(Dynamic Bayesian Network,DBN)模型的唇读系统。
短句来源
    A Serial Decoding Method Based on Bayesian Network
    基于贝叶斯网络的串行译码方法
短句来源
    Adjust fire goal with UAV based on dynamic bayesian network
    基于动态贝叶斯网络的目标侦察信息处理研究
短句来源
    Bayesian Network Theory Based Blind Source Separation from Time-Varying Mixture
    基于贝叶斯网络理论的时变混合盲源分离算法
短句来源
    Recognition Method of Radar Emitters based on Bayesian Network Classifiers
    基于贝叶斯网络分类器的雷达辐射源识别方法
短句来源
更多       
  “bayesian network”译为未确定词的双语例句
    With the combination of turbo decoding with graph,the authors propose to describe the process of turbo decoding using Bayesian network model,Based on this model,a parellel turbo decoding algorithm is established using Pearls belief propagation algorithm.
    研究了Turbo码的并行译码算法 ,将Turbo码译码和图论结合起来 ,利用Bayesian网络图模型描述了Turbo码的译码过程 ,基于模型使用Pearl的信息传播算法 ,建立了Turbo码的并行译码算法。
短句来源
查询“bayesian network”译词为用户自定义的双语例句

    我想查看译文中含有:的双语例句
例句
为了更好的帮助您理解掌握查询词或其译词在地道英语中的实际用法,我们为您准备了出自英文原文的大量英语例句,供您参考。
  bayesian network
This paper addresses issues in constructing a Bayesian network domain model for diagnostic purposes from expert knowledge.
      
Tailored-to-Fit Bayesian Network Modeling of Expert Diagnostic Knowledge
      
The object class is generatively modeled using a simple Bayesian network with a central hidden node containing location and scale information, and nodes describing object parts.
      
Our algorithm is implemented in a commercial Bayesian Network software package, AgenaRisk, which allows model construction and testing to be carried out easily.
      
In particular, to determine Jeffrey's prior for this model family, we show how to compute the (expected) Fisher information matrix for a fixed but arbitrary Bayesian network structure.
      
更多          


The turbo decoding algorithms can be divided into two categories of serial turbo decoding and parallel turbo decoding.The serial turbo decoding algorithms,such as MAP,LOG MAP etc,have been thoroughly studied.For the parallel turbo decoding algorithm,however,there are still a lot to be studied.With the combination of turbo decoding with graph,the authors propose to describe the process of turbo decoding using Bayesian network model,Based on this model,a parellel turbo decoding algorithm is established...

The turbo decoding algorithms can be divided into two categories of serial turbo decoding and parallel turbo decoding.The serial turbo decoding algorithms,such as MAP,LOG MAP etc,have been thoroughly studied.For the parallel turbo decoding algorithm,however,there are still a lot to be studied.With the combination of turbo decoding with graph,the authors propose to describe the process of turbo decoding using Bayesian network model,Based on this model,a parellel turbo decoding algorithm is established using Pearls belief propagation algorithm.the simulation results have shown find that proposed parallel turbo decoding algorithm is superior to serial ones in terms of decoding performance.

Turbo码的译码算法大致可分为串行译码算法和并行译码算法两大类。串行译码算法如MAP、LOG MAP等的研究已比较深入。但并行译码算法 ,尚有许多问题有待探讨。研究了Turbo码的并行译码算法 ,将Turbo码译码和图论结合起来 ,利用Bayesian网络图模型描述了Turbo码的译码过程 ,基于模型使用Pearl的信息传播算法 ,建立了Turbo码的并行译码算法。并对所讨论的并行译码算法进行了模拟 ,模拟结果表明 :该并行译码在译码性能等方面比串行译码优越

Dynamic Bayesian Networks are a powerful methodology for representing and computing uncertain problem of stochastic processes.The changing situation complicates information disposal and impacts on direction and manner of decision-making.The paper brings out that combine Hidden Markov Models with Fuzzy inference to come into being Dynamic Bayesian Networks and use the graph to analyse spy information that gained from war field.Firstly,we can build a battlefield dynamic model based on Dynamic Bayesian...

Dynamic Bayesian Networks are a powerful methodology for representing and computing uncertain problem of stochastic processes.The changing situation complicates information disposal and impacts on direction and manner of decision-making.The paper brings out that combine Hidden Markov Models with Fuzzy inference to come into being Dynamic Bayesian Networks and use the graph to analyse spy information that gained from war field.Firstly,we can build a battlefield dynamic model based on Dynamic Bayesian Networks.Secondly,we use a Viterbi algorithm to inference and get the best estimate about hidden sequence.At the same time,we can predict trend of changing war field for the future.The next step,we can use Fuzzy inference to get the best decision or to apply decision-making first.In the end,we do an emulational a examination to prove the idea is right.

 动态贝叶斯网络是对具有随机过程性质的不确定性问题进行建模和处理的有力工具.战场环境随机变化使得侦察信息处理变的复杂化,从而影响决策的具体方式或决策方向.本文提出将隐马尔可夫模型图形模式与模糊推理结合起来构成DBN,将其用于战场侦察情报的推理分析.首先建立动态贝叶斯网络的环境变化感知模型,而后应用Viterbi解码算法获得当前隐含序列最优估计,通过HMM状态转移矩阵可预测出未来环境变化趋势,最后应用模糊推理得到问题的最优的决策或优先采用的解决方式.仿真结果表明了模型的可行性.

The"discrete states assumption"and"conditional independent assumption"are two main limitations in standard hidden Markov model for speech recognition.The former ignores time-short stationarity of speech signals and the latter disconsiders the intra-frame correlation between the feature vector elements.This paper investigates the combined traditional hidden Markov modeling technology with mixture of factor analysis.It proposes a hidden Markov model based on mixture of factor analysis(HMM-MFA) and use a dynamic...

The"discrete states assumption"and"conditional independent assumption"are two main limitations in standard hidden Markov model for speech recognition.The former ignores time-short stationarity of speech signals and the latter disconsiders the intra-frame correlation between the feature vector elements.This paper investigates the combined traditional hidden Markov modeling technology with mixture of factor analysis.It proposes a hidden Markov model based on mixture of factor analysis(HMM-MFA) and use a dynamic Bayesian networks for this approach.A number of standard models including HMM-DG(Hidden Markov Model Based Diagonal Gaussian distributions),HMM-FA(Hidden MarkovModel Based Factor Analysis) and other currently very popular acoustic models are forms of HMM-MFA with different configurations.

经典隐马尔可夫模型用于语音识别存在的两个主要缺陷是“离散状态假设”和“独立分布假设”。前者忽略了语音信号的非平稳性,后者忽略了语音信号的相关性。文章将混合因子分析方法用于语音建模,提出了基于混合因子分析的隐马尔可夫模型框架,并用动态贝叶斯网络形象地表示。该模型框架不仅从理论上解决了上述问题,而且给出许多语音建模的选择。目前广泛使用的统计声学模型均可视为该模型的特例。

 
<< 更多相关文摘    
图标索引 相关查询

 


 
CNKI小工具
在英文学术搜索中查有关bayesian network的内容
在知识搜索中查有关bayesian network的内容
在数字搜索中查有关bayesian network的内容
在概念知识元中查有关bayesian network的内容
在学术趋势中查有关bayesian network的内容
 
 

CNKI主页设CNKI翻译助手为主页 | 收藏CNKI翻译助手 | 广告服务 | 英文学术搜索
版权图标  2008 CNKI-中国知网
京ICP证040431号 互联网出版许可证 新出网证(京)字008号
北京市公安局海淀分局 备案号:110 1081725
版权图标 2008中国知网(cnki) 中国学术期刊(光盘版)电子杂志社