介绍了专家系统的具体实现过程，对基于规则、基于案例、基于模型以及混合推理的推理机制进行了研究。
It introduces the concrete realization course of the expert system and traverses the inference mechanism based on rule, case, model and hybrid reasoning.
与传统的基于规则的系统相比，基于案例推理的系统具有简化知识获取、便于知识积累等优点。
Comparing to traditional rule-based system, it has many advantages, which can predigest knowledge gathering and accumulate knowledge easily.
通过对基于规则、模型与案例推理的分析，给出了基于混合推理策略的半智能CAFD系统总体结构。
Through the analysis on ruled based and model based and case based reasoning, an overall structure of semi intelligent CAFD system based on mixed reasoning strategy is presented.
分析了当前基于规则的专家系统存在的问题，介绍了基于案例推理（CBR）的概念。
The features and problems of the current rule_based expert systems is firstly summarized, and then an introduction to Case_Based Reasoning (CBR)is given.
文章提出了毛纺织工艺设计智能化模型，通过基于案例(CBR)和基于规则的推理(rbr)以及人工神经网络(ANN)等关键技术的引入，提高了系统解决实际问题的能力。
This paper presents the wool textile process intelligent design model (WTPIDM), by the introduction of CBR, RBR, ANN technologies, improving the system capability to solve the problems.
传统的钻井专家系统采用基于规则的推理，对于难以规则化的典型案例不能够充分利用。
The traditional drilling expert system is based on rule, and the typical cases that are difficultly ruled could not be used enough.
提出了基于知识的毛纺产品工艺设计智能模型，通过基于案例（C BR）和基于规则的推理（RBR）以及人工神经网络（ANN）等关键技术的引入，提高了系统解决实际问题的能力。
This paper presents the wool textile process intelligent design model(WTPIDM), by the introduction of CBR, RBR, ANN technologies, improving the system capability to solve the problems.
基于规则的推理（RBR）和基于案例的推理（CBR）则是知识工程中两类实用有效的推理方式。
Rule-based and case-based reasoning are two important efficient reasoning methods in artificial intelligence field.
基于规则的推理（RBR）和基于案例的推理（CBR）则是知识工程中两类实用有效的推理方式。
Rule-based and case-based reasoning are two important efficient reasoning methods in artificial intelligence field.
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