粗糙集理论是继概率论、模糊集、证据理论之后的又一个处理不确定性问题的新型数学工具。
Rough set theory is a new mathematical tool to deal with vagueness and Uncertainty problem after probability theory, fuzzy sets, mathematical theory of evidence.
证据理论和模糊积分融合方法可以减少决策过程中的不确定性，大大的提高了决策的精度。
The evidence theory and fuzzy integral can abate the incertitude and improve the accuracy of decision.
基于证据理论的电子装备故障诊断，采用信任函数作为不确定性度量工具。
Fault diagnosis of electronic equipment based on evidence theory, the trust function is adopted as uncertainty measurement tool.
证据理论是一种重要的不确定性推理方法。
DS evidence theory is an important method in uncertainty reasoning.
粗糙集理论与证据理论都是处理不确定性知识的数学工具。
Both rough set theory and evidence theory are the math tools for dealing with uncertain knowledge.
粗集理论是继概率论、模糊集、证据理论之后的又一种处理模糊和不确定性知识的数学工具。
Rough Set Theory is another mathematical tool used for dealing with fuzzy and uncertain knowledge besides Probability Theory, fuzzy Set Theory and Evidence Theory.
证据理论在不确定性问题中有很强的推理能力，而且合成法则对数据的融合处理提供理论依据。
The theory of evidence, called D-S theory, has great reasoning capacity in the questionably problems, and the dempster rule can provide theoretical foundation for the synergetic disposal of data.
该推理模型前级采用神经网络并行子网，用于目标的预分类，后级采用证据理论用于多周期的不确定性推理和概率的全局分配。
The forestage of the fusion model completes target presort and its post-stage is used to multi-period uncertainty inference and the whole set distribution of probability.
证据理论是一种适用于不确定决策问题和风险型决策问题的决策方法。
The evidence theory is a decision-making method used to solve uncertain and risk decision-making problems.
本文主要涉及的不确定推理模型包括主观贝叶斯的概率推理模型，可信度理论推理模型，证据理论及其改进推理模型以及神经网络推理模型。
In the paper, the models of uncertain reasoning are focused, such as the reasoning model of Bayes probability, Reliability theory, D-S evidence theory and Neural Network.
当概率已知时，证据理论就变成了概率论，它更符合对专家不确定性信息的融合。
When the probability is known, the theory of evidence becomes a theory of probability, which is more appropriate for the integration of uncertain information.
证据理论是一种处理不确定性方法，该理论基于专家的知识和经验进行推理，适用于存在主观不确定因素的决策。
Evidence theory is a method for uncertainty. It can depend on experts' opinion for reasoning, suitable for decision of subjective uncertainty.
实验结果表明，D - S证据理论决策树分类算法能有效地对不确定数据进行分类，有较好的分类准确度，并能有效避免组合爆炸。
This D-S decision tree is a new classification method applied to uncertain data and shows good performance and can efficiently avoid combinatorial explosion.
实验结果表明，D - S证据理论决策树分类算法能有效地对不确定数据进行分类，有较好的分类准确度，并能有效避免组合爆炸。
This D-S decision tree is a new classification method applied to uncertain data and shows good performance and can efficiently avoid combinatorial explosion.
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