基于事例推理的电路诊断技术研究

本文首先简要介绍了基于事例推理(CBR)和基于规则推理(RBR)的优缺点，其次建立了一个CBR和RBR相结合的电路故障诊断系统，最后说明了该系统的基本结构及设计过程。     

基于实例的智能工艺设计系统

针对传统智能工艺设计系统的缺陷与不足，结合基于实例推理（Case-Based Reasoning,CBR）和基于规则推理（Rule-Based Reasoning,RBR)的方法，设计了一个基于实例的智能工艺设计系统，给出了工艺实例一个完整清晰的形式化描述，阐述了新零件与实例进行比较和匹配的策略和算法，在检索出相符的实例后，调用RBR方法对实例进行修正，最终完成复杂的工艺设计任务。     

基于案例与规则推理的故障诊断专家系统

设计并实现基于案例的推理(CBR)与基于规则的推理(RBR)的故障旋转机械诊断专家系统。采用CBR与RBR串行方式进行推理,优先通过案例匹配方式寻求诊断结果,在不适用情况下转入通用性规则推理,并将诊断结果反馈给知识库进行优化。应用结果表明,该系统诊断结果与实际相符合,且诊断速度快、针对性强。     

上下文推理技术在智能家庭中的研究与应用

针对智能家庭具有预感知能力、可以为用户提供主动的智能服务的特点,建立一个智能家庭本体模型,利用Jena框架的推理接口实现上下文的基本推理,通过在推理层和知识库之间增加一个上下文过滤器,有效过滤掉推理过程中产生的无用数据。实验结果验证了规则推理在智能家庭中的可行性和有效性。     

一种CBR与RBR相结合的快速预案生成系统

将范例推理(case based reasoning,CBR)与规则推理(rule based reasoning,RBR)两种人工智能技术相结合,实现一种快速预案生成系统.它有效地解决了单纯RBR系统在预案生成过程中的时间延迟缺陷和知识库难以获取的瓶颈.通过CBR工具,能够把以前发生的紧急事件和解决方案生成预案.一旦新的事件发生,首先从预案库中进行案例的相似性检索,如果没有检索到预案或者检索到的预案匹配度很低,再采用RBR系统对紧急事件进行规则推理,然后把推理结果重新存入预案库.实验数据表明,这种方法对单纯RBR系统在时间响应上进行了有效的优化.另外,因为案例的获取比专家系统推理规则的获取容易得多,它同时解决了RBR系统推理规则难以获取的瓶颈.根据这种思想,实现了CBR与RBR结合的快速预案生成系统.目前,它已经应用到抗洪抢险的预案生成和城市应急联动的决策支持上,效果表明它在预案生成速度以及实际可操作性上都具有明显优势.     

船舶避碰智能决策支持系统研究

建立了一个基于RBR(Case-Based Reasoning,基于案例推理)和CBR(Rule-Based Reasoning,基于规则推理)结合的船舶避碰决策支持模型,将这个模型引入到船舶避碰智能决策支持系统IDSSVCA(Intelligent Decision Support System for Vessel...     

智能环灾应急决策支持技术研究

针对目前环灾应急决策系统中预案信息化程度低、动态应急能力不足的问题,文中在数字化预案的基础上,提出了一套基于案例推理(CBR)和基于规则推理(RBR)相结合的智能辅助决策机制。鉴于环灾应急事件的复杂性,许多应急处置方案都是基于过去的经验,而且应急过程中又随时有可能出现新的应急目标,文中采用了以CBR方法为主,RBR方法为辅的CR智能辅助决策方法。这种方法能更有效地实现复杂环灾应急的辅助决策需要,最后通过某化工园应急决策实例展示了文中所讨论的方法的过程。     

基于案例推理和规则推理的公安突发事件辅助决策算法

为了提高突发事件发生时公安指挥部门处置决策方案的及时性和科学性,本文提出基于案例推理(Case-Based Reasoning, CBR)和规则推理(Rule-Based Reasoning, RBR)的公安突发事件辅助决策算法。算法根据突发事件的级别、类型和突发事件中的具体数据,如伤亡人数等,通过CBR检索出案例库中同级别同类型的最相似案例,再通过RBR对检索案例的结果进行修正优化使之更适用于突发事件的实际情况。最后通过实例成功地验证了该算法。该算法能够为公安应急预案与辅助决策平台的建设提供参考。     

基于CBR与RBR混合推理的仪表设计专家系统

针对仪表设计领域知识和经验的重用和共享性差的问题，完成了仪表设计专家系统的开发。对该系统的功能模型作了详细的阐述，并对知识库的设计和推理机制进行了深入研究，提出了一种将CBR与RBR相结合的新型推理方法，该方法结合两种推理方式的优势，提高了推理的质量和效率。实验表明，该系统能够根据用户的要求进行仪表设计，具有广泛的应用前景。     

基于觉察上下文计算的健康智能家庭研究

利用觉察上下文计算技术来研究实现健康智能家庭,主要研究了健康智能家庭的上下文建模和上下文推理,并构建了一个实验系统AngelHome,分析了健康智能家庭中的各种上下文信息,利用本体技术对其进行建模,并用OWLDL语言表达上下文信息模型,构建了AngelHome本体;在上下文推理部分采用混合推理,对不同的推理任务分别采用本体推理机、自定义规则推理机和贝叶斯神经网络推理.AngelHome采用OSGi框架,具有良好的伸缩性,这里分析了系统的几个主要部分,并进行了测试.实验结果表明,利用觉察上下文计算技术来实现健康智能家庭是可行的.     

ASHSD-II: A computational model for litigation support

Abstract: In this paper a hybrid knowledge-based system which exploits both rule-based reasoning (RBR) and case-based reasoning (CBR) is presented. The issues of RBR and CBR in general in the context of legal knowledge-based systems and legislation in rule form and previously-decided cases in an interconnected graph form are discussed. It is possible for the user to select either reasoning method (RBR or CBR), or indicate no preference. The rule base of this system consists of two types of rule. The first type of rule determines which options are legally applicable. The second type indicates how the courts are likely to act within the range of options available, which is determined by the first type of rule. When CBR is selected, the system uses the features of previously-decided cases to select the most similar cases to the situation that is described in the input and displays their details of decisions. In case of the selection of no preference option, the system applies RBR and CBR method separately, and then presents results based on an automated relative rating of the qualities of the RBR (based on the second type of rules) and CBR advice. These ideas have been implemented in a prototype system, known as A dvisory S upport for H ome S ettlement in D ivorce (ASHSD-II).     

Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis

Whenever there is any fault in an automotive engine ignition system or changes of an engine condition, an automotive mechanic can conventionally perform an analysis on the ignition pattern of the engine to examine symptoms, based on specific domain knowledge (domain features of an ignition pattern). In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem using not only the domain features but also the extracted features of signals captured using a computer-linked automotive scope meter. CBR expert system has the advantage that it provides user with multiple possible diagnoses, instead of a single most probable diagnosis provided by traditional network-based classifiers such as multi-layer perceptions (MLP) and support vector machines (SVM). In addition, CBR overcomes the problem of incremental and decremental knowledge update as required by both MLP and SVM. Although CBR is effective, its application for high dimensional domains is inefficient because every instance in a case library must be compared during reasoning. To overcome this inefficiency, a combination of preprocessing methods, such as wavelet packet transforms (WPT), kernel principal component analysis (KPCA) and kernel K-means (KKM) is proposed. Considering the ignition signals captured by a scope meter are very similar, WPT is used for feature extraction so that the ignition signals can be compared with the extracted features. However, there exist many redundant points in the extracted features, which may degrade the diagnosis performance. Therefore, KPCA is employed to perform a dimension reduction. In addition, the number of cases in a case library can be controlled through clustering; KKM is adopted for this purpose. In this paper, several diagnosis methods are also used for comparison including MLP, SVM and CBR. Experimental results showed that CBR using WPT and KKM generated the highest accuracy and fitted better the requirements of the expert system.     

The development and utilization of the case-based help-desk support system HOMER

Current case-based reasoning (CBR) process models present CBR as a low-maintenance AI-technology and do not take the processes that have to be enacted during system development and utilization into account. Since a CBR system can only be useful if it is integrated into an organizational structure and used by more than one user, processes for continuous knowledge acquisition, utilization and maintenance have to be put in place. In this paper the shortcomings of classical CBR process models are analyzed, and, based on the experiences made during the development of the case-based help-desk support system HOMER, the managerial, organizational and technical processes related to the development and utilization of CBR systems are described.     

案例推理的故障诊断技术研究综述

基于案例推理(case-based reasoning,CBR)的故障诊断作为一种新的智能诊断技术,模拟人类求解问题的思路,通过历史案例发现新问题的解。概述了CBR的理论基础和基本原理;从工作过程和集成框架两个方面综述了CBR故障诊断技术的研究现状,其中工作过程包括案例的表示、检索和重用,以及案例库的维护,集成框架包括CBR与基于规则推理、CBR与人工神经网络以及CBR与多智能体等三种情况;比较了六种故障诊断技术的特点及应用范围;总结了CBR故障诊断技术有待解决的问题。     

Incremental development of CBR strategies for computing project cost probabilities

This paper presents three CBR systems that have been developed over seven years in collaboration with two industrial partners. In this research, case based reasoning (CBR) is used to compute costs of construction projects. In contrast with previous work in the field of CBR, the focus is on choosing strategies that are compatible with user needs and characteristics. Comparing the three strategies reveals advantages and drawbacks while illustrating a “real-life” evolution of a CBR architecture in an industrial context. An important conclusion is that the ways users perform tasks have a direct influence on the best architecture for the CBR system (e.g. transformational/derivational analogy). Incremental development of strategies in the final system improves user interaction, expedites time consuming tasks and favours identification of synergy between techniques such as CBR and data mining.     

案例推理属性权重的分配模型比较研究

案例推理系统中各属性权重的赋值决定了案例之间的相似度 大小,进而对推理结果的正确与否产生显著影响.以属性加权K-最近邻 相似案例检索为基础,讨论了使用注水原理分配属性权重的机理,并通过建 立权重分配的合理性指标,构造拉格朗日函数对权重进行优 化求解,得到了收敛的注水分配算法.通过五折交叉的模式分类实验 ,分别对属性权重的平均分配法、注水分配算法和遗传算法分配法进行了比较研究,案例推理分类结果证明,在引入注水分配算法后,其分类性能得到有效改善.     

Hydraulic Circuit Design and Dynamic Learning Using Case-based Reasoning

This paper describes the design and implementation of a hydraulic circuit design system using case-based reasoning (CBR) paradigm from AI community The domain of hydraulic circuit design and case-based reasoning are briefly reviewed Then a proposed methodology in compuer-aided circuit design and dynamic leaning with the use of CBR is described Finally an application example is selected to illustrate the ussfulness of applying CBR in hydraulic circuit design with leaming.     

A hybrid system applied to epidemic screening

Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), human experts rarely use a single type of knowledge to solve a real-world problem. A human expert usually combines a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in the intelligent systems area. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid epidemic screening KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed. The system has been tested using real epidemic screening variables and data.     

Case-based reasoning and adaptation in hydraulic production machine design

Case-based reasoning (CBR) can support hydraulic circuit design. Existing expert systems for hydraulic system design use production rules as its source of knowledge. However, this leads to problems of knowledge acquisition and knowledge base maintenance. This paper describes the application of CBR to hydraulic circuit design for production machines, which helps solving problems using past experience. A technique Case-based adaptation (CBA) is implemented in the adaptation stage of CBR so that adaptation becomes much easier. A prototype system has been developed to verify the usefulness of CBR and CBA in hydraulic production machines.     

A hybrid knowledge-based system applied to epidemic screening

Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), experts rarely use a single type of knowledge in solving a problem. More often, an expert will apply a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in artificial intelligence. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed for epidemic screening. The system has been tested using real data, and results are encouraging.