推理机制的量化是通过引入概率信息实现的。
The reasoning mechanism is quantified by introducing information of probability.
将学习矢量量化神经网络集成在基于实例推理的故障诊断方法中，减小了实例搜索空间，提高了实例检索效率。
The learning vector quantization neural network has been integrated successfully with the case-based reasoning approach to reduce the case indexing space and to enhance the indexing efficiency.
结果表明，高层在价值判断方面比中层人员要更加准确，而中层人员在常识判断，演绎推理，量化表达和精确性方面的能力优于高层人员。
Test results indicate top management has strength in value judgment, while middle management is more competitive in aspects of common sense, deductive reasoning, quantitative expression and accuracy.
基于这种模糊控制关系和模糊推理规则，得到了被测软件通过测试的标准值，使测试通过的判定得以量化；
The standard value of the tested software to pass the test was then obtained from the fuzzy control relation and fuzzy inference rules, thus quantifying the judgment for software to pass the test.
三段论是量化的间接关系推理。
开展了设计功能域建模研究，得到用于设计推理的量化设计要求。
In order to obtain the quantitative design demand used for the design reasoning, function modeling of design is produced.
针对量化参数间映射关系，提出了基于定性推理和基于数据挖掘的启发性知识获取策略。
According to quantitative parameters mapping relation, heuristic knowledge acquisition strategy based on qualitative reasoning and data mining is proposed.
本文采用模糊似然推理原理，对引起模糊性的因素进行定量化分析，客观地评价了结构的安全性。
The factors which cause fuzziness are analysed quantitatively and the Safety of Structure is assessed objectively by means of the principle of fuzzy reasoning.
本文重点是机电产品色彩设计知识表示、知识获取策略、配色推理机制、配色方案评判模型以及色彩情感效应的量化研究。
In this paper, emphasis is laid on the researches of knowledge representation, knowledge obtaining tactics, color scheme reasoning, color scheme evaluation modeling and color sensibility evaluation.
算法用贝耶斯推理处理不确定信息，量化地评估系统安全状态，并且有效地消除误报。
The algorithm handles uncertain information with Bayesian inference, giving a quantitative evaluation of the security state of a system and eliminating false alarms effectively.
从推理机制的量化和推理环境的优选两方面完善了该理论，使其诊断能力、实时性和适用范围都得到提高。
By quantifying the reasoning mechanism and optimizing the reasoning environments, it is improved further in diagnosis capability, real time performance and application scope.
从推理机制的量化和推理环境的优选两方面完善了该理论，使其诊断能力、实时性和适用范围都得到提高。
By quantifying the reasoning mechanism and optimizing the reasoning environments, it is improved further in diagnosis capability, real time performance and application scope.
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