The new auto malfunction diagnosis model is based on the fuzzy theory, probability reasoning and parsimonious covering theory.
将模糊数学、概率推理和节约覆盖集理论引入到汽车故障诊断中，建立了一个新的汽车故障综合诊断模型。
At the same time, probability reasoning can be realized by BN, which can be used to describe changes of fault symptoms and analyze fault reasons of transformer.
同时利用贝叶斯网络实现概率推理，便于描述故障特征的变化及对变压器故障原因的快速分析。
Probability theory captures a number of essential characteristics of human cognition, including aspects of perception, reasoning, belief revision, and learning.
机率理论涵盖了人类认知中的许多重要特征，包括感知、推理、信念改变和学习方面。
The fundamental tools of A.I. shifted from Logic to Probability in the late 1980s, and fundamental progress in the theory of uncertain reasoning underlies many of the recent practical advances.
研究人工智能的基础工具在80年代后期从逻辑转向了概率，而关于不确定性的理论研究的进展成为了近期许多应用的基础。
The fundamental tools of A. I. shifted from Logic to Probability in the late 1980s, and fundamental progress in the theory of uncertain reasoning underlies many of the recent practical advances.
研究人工智能的基础工具在80年代后期从逻辑转向了概率，而关于不确定性的理论研究的进展成为了近期许多应用的基础。
After analyzing several theory models of inductive reasoning, we use the Bayes Theorem to prove the premise probability principle, and integrate this theory with human mental process.
在分析多个理论模型的基础上，采用贝叶斯定理证明了前提概率原则，并将此原则与人类心理过程相结合，将归纳推理分解为连续进行的三步过程。
The paper has improved the contexture methodology of basic probability of D-S Reasoning when its application in information temporal-spatial fusion.
论文改进了应用证据理论进行信息时空融合诊断时基本概率赋值的构造方法。
The reasoning mechanism is quantified by introducing information of probability.
推理机制的量化是通过引入概率信息实现的。
Heuristic probability estimate is one of the popular issue of natural reasoning in foreign psychological research currently.
启发式概率估计是当前国外自然推理研究的新兴的热点问题之一。
In the first place, the expert system diagnoses the system in high-level one (the thick grain size) and goes on expert reasoning, for the sake of reducing the failure probability.
即首先由专家系统对系统进行高层次(粗粒度)诊断，进行专家的推理，把故障的可能性缩小。
The characteristic of the Bayes method is to use probability to express the uncertainty of all forms, learning and the reasoning of other forms are all realized with the rule of probability.
贝叶斯方法的特点是使用概率去表示所有形式的不确定性，学习或其他形式的推理都用概率规则来实现。
Probability theory is the only reasonable way to represent uncertainty, it is useful for machine learning or reasoning under uncertainty.
概率论是表示不确定性的唯一合理的方法，概率论对于机器学习或不确定情况下的推理是有用的。
The key problem to D-S reasoning is basic probability assignment function, so the algorithm implementation of D-S reasoning is a esrious problem.
在基于D-S推理的信息融合中，其关键问题是基本概率赋值函数的构造。
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.
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。
This paper analyzes the characteristics and limitations of approximate reasoning (based on probability) and nonmonotonic reasoning (based on truth maintenance system).
不精确推理和非单调推理是人工智能中两个重要的研究方面。
Considering some inferences that are valid with a certain degree of probability in linguistic communication, the paper tries to argue that everyday reasoning depends on more than logic.
基于语言交际中推理的有效性具有某种概率性，本文提出日常推理不只依赖于逻辑。
And the robustness of DHAM was derived using probability and statistic method, with some approximate reasoning using.
运用概论统计的方法推导出了运行于同步方式下D H A M 的鲁棒性，推导中对结果进行了近似处理。
And the robustness of DHAM was derived using probability and statistic method, with some approximate reasoning using.
运用概论统计的方法推导出了运行于同步方式下D H A M 的鲁棒性，推导中对结果进行了近似处理。
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