基于案例的推理技术研究进展及应用

对基于案例的推理(CBR)近年来的国内外理论和应用研究进展做出了综述与评价.理论方面主要包括基于案例推理中知识表示,基于案例的解释、案例提取失败和恢复,案例改编和维护以及基于案例推理技术与其它人工智能技术的比较研究等;介绍了CBR在计算机与信息科学、生物学和医学等领域国内外的应用情况,并对其它一些领域的应用情况作了概括性说明;最后总结并预测了基于案例推理技术的未来可能的研究方向.     

基于ANN与CBR相结合的复杂装备故障诊断研究

在分析了神经网络(ANN)方法与案例推理(CBR)方法的特点和互补性的基础上,设计了基于ANN与CBR相结合的复杂装备故障诊断模型.将人工神经网络方法融入CBR推理的故障库分类、案例检索、案例修改等多个阶段中,较好地解决了复杂电子装备故障诊断的快速与准确问题.最后通过对雷达情报综合电子信息系统故障实例的诊断仿真,验证了该算法的有效性.     

基于CBR的火控雷达故障诊断专家系统研究

将基于案例推理(CBR)和专家系统理论应用到故障诊断领域,对雷达装备的故障诊断问题进行研究,形成一个集成的智能诊断专家系统.本文浅析了基于案例推理,介绍了系统的总体结构和工作原理,并给出了具体的诊断实例.利用CBR技术建立的故障诊断专家系统可提高雷达装备故障诊断的正确性和效率.     

TCBR中案例相关度及案例冗余处理模型

首先介绍基于文档的案例推理技术TCBR，以及CRN、IEs等基本理论，然后详细介绍CRN的构建过程和相关的函数模型，并提出一个求和函数来计算案例与当前问题的相关值，在文章的后面阐述了TCBR的案例学习方法以及案例相似程度的一个判断函数．并提出在这种情况下对案例冗余的处理方案。     

TCBR中案例相关度及案例冗余处理模型

首先介绍基于文档的案例推理技术TCBR,以及CRN、IEs等基本理论,然后详细介绍CRN的构建过程和相关的函数模型,并提出一个求和函数来计算案例与当前问题的相关值,在文章的后面阐述了TCBR的案例学习方法以及案例相似程度的一个判断函数,并提出在这种情况下对案例冗余的处理方案。     

灰度关联理论在CBR中的应用研究

针对基于规则推理技术(RBR)知识获取困难、自学习能力差等缺陷,将基于案例推理技术(CBR)引入故障诊断系统中.介绍了基于案例推理的故障诊断方法的工作机理和过程模型,阐述了案例表示、案例检索、案例保存和案例库维护机制,然后简单介绍了灰色关联理论知识,并把灰色关联理论应用到故障案例相似度的计算中.根据实验结果可知,该方法有效地改进了案例检索算法,提高了故障案例匹配的准确度和检索效率,同时具有较好的分辨率.     

基于遥感案例推理的海岸带养殖信息提取

目前基于目视解释或光谱分类的养殖信息提取效率低,难以克服由于地物混杂带来的“椒盐”噪声现象且难以融合地学知识。针对养殖信息提取中存在的问题,首先在分析现有养殖信息提取方法和案例推理CBR(Case\|Based Reasoning)用于遥感图像处理的基础上,提出基于遥感案例推理的海岸带养殖信息提取的研究思路;其次,结合养殖区域的空间特征和属性特征,构建案例的表达模型以及CBR相似性推理模型;最后,对不属于案例构建区的粤西沙田镇进行养殖信息提取的CBR实验,精度达到84.56%。对比CBR方法和传统监督分类方法可知,CBR方法是实现海岸带养殖信息快速准确提取的一种有效手段。     

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。     

分布式知识系统中基于粗糙集的CBR检索方法

基于实例推理(Case-Based Reasoning,CBR)自20世纪80年代末、90年代初兴起之后,受到人工智能研究者的高度重视.CBR是一种相似推理方法,其核心在于用过去的实例和经验来解决问题[1].如何在CBR系统中高效地完成最相似实例的检索是CBR的关键问题之一,这对于新问题的求解效率和推理的准确性都有较大影响.     

基于案例推理的工作流异常处理研究

对工作流的异常和案例推理(Case-Based Reasoning,简称CBR)的机制进行了介绍,给出了一个应用CBR技术进行异常处理的工作流模型,并研究了应用CBR方法处理工作流异常的机制。     

Case-Based Reasoning System and Artificial Neural Networks: A Review

In this survey paper, the-state-of-art of the connectionist model (i.e. Artificial Neural Network (ANN)) based methodology for a Case-Based Reasoning (CBR) system design is discussed. Special emphasis is laid on how the ANN can advance CBR technology by building an ANN-based CBR system, or integrating itself as a component within a CBR system. Several ANN models proposed for constructing a CBR system and for solving some special issues involved in a CBR process are described. The main characteristics of each model are analysed, and the advantages and limitations of different models are compared. Also, future research directions are outlined.     

Integrating a case-based reasoning shell and Web 2.0: design recommendations and insights

The design and implementation of case-based reasoning (CBR) applications is time-consuming. To facilitate the development of CBR applications in various problem domains, the CBR community has created a number of CBR shells and software frameworks in the past twenty years. This paper provides a review of the state-of-the-art of CBR shells and software frameworks, highlights why the integration of Web 2.0 and CBR development tools is useful, and gives an example as to how we implement such integration. We use this example to illustrate how Web 2.0 features such as blogging functions can be integrated in a CBR system. Design recommendations and insights for implementing a Web 2.0-based CBR shell are also provided.     

On performance of case-based reasoning in Chinese business failure prediction from sensitivity,specificity, positive and negative values

Case-based reasoning (CBR) is a machine learning technique of high performance in classification problems, and it is also a chief method in predicting business failure. Recently, several techniques have been introduced into the life-cycle of CBR for business failure prediction (BFP). The drawback of former researches on CBR-based BFP is that they only use total predictive accuracy when assessing CBR. In this research, we provide evidence on performance of CBR in Chinese BFP from various views of sensitivity, specificity, positive and negative values. Data are collected from Shanghai Stock Exchange and Shenzhen Stock Exchange in China. And we present how data are preprocessed from the view of data mining. The classical CBR model on the base of Euclidean metric, the grey CBR model on the base of grey coefficient metric, and the pseudo CBR model on the base of pseudo outranking relations are employed to make a comparative study on CBR's predictive performance in BFP. Meanwhile, support vector machine (SVM) is employed to be a baseline model for comparison. The results indicate that pseudo CBR produces better performance in Chinese BFP than classical CBR and grey CBR significantly on the whole, and it outperforms SVM marginally by total predictive accuracy and sensitivity, while it is not significantly worse than SVM by specificity.     

基于事例推理的技术及其应用前景

文章从基于事例推理(CBR)的研究者及使用者的角度,介绍了目前在CBR中较成熟的技术,重点介绍了事例表示、事例检索、事例修改以及 CBR开发工具等,并分析了其中的不足,指出了CBR的发展前景。     

Two-step filtering datamining method integrating case-based reasoning and rule induction

Case-based reasoning (CBR) methods are applied to various target problems on the supposition that previous cases are sufficiently similar to current target problems, and the results of previous similar cases support the same result consistently. However, these assumptions are not applicable for some target cases. There are some target cases that have no sufficiently similar cases, or if they have, the results of these previous cases are inconsistent. That is, the appropriateness of CBR is different for each target case, even though they are problems in the same domain. Thus, applying CBR to whole datasets in a domain is not reasonable. This paper presents a new hybrid datamining technique called two-step filtering CBR and rule induction (TSFCR), which dynamically selects either CBR or RI for each target case, taking into consideration similarities and consistencies of previous cases. We apply this method to three medical diagnosis datasets and one credit analysis dataset in order to demonstrate that TSFCR outperforms the genuine CBR and RI.     

Improving user experience with case-based reasoning systems using text mining and Web 2.0

Many CBR systems have been developed in the past. However, currently many CBR systems are facing a sustainability issue such as outdated cases and stagnant case growth. Some CBR systems have fallen into disuse due to the lack of new cases, case update, user participation and user engagement. To encourage the use of CBR systems and give users better experience, CBR system developers need to come up with new ways to add new features and values to the CBR systems. The author proposes a framework to use text mining and Web 2.0 technologies to improve and enhance CBR systems for providing better user experience. Two case studies were conducted to evaluate the usefulness of text mining techniques and Web 2.0 technologies for enhancing a large scale CBR system. The results suggest that text mining and Web 2.0 are promising ways to bring additional values to CBR and they should be incorporated into the CBR design and development process for the benefit of CBR users.     

基于规则的IDS中的CBR研究

本文在入侵检测系统（IDS）中引入基于案例的推（CBR）来降低基于规则的精确匹配所造成的漏报率,有效地检测由已知攻击变异成的攻击。描述了实现CBR的步骤;给出了由规则设计和构造案例库的启发式方法;分析了实现CBR的有关算法;最后给出在入侵检测系统Snort上扩充CBR功能的实验结果。     

A CBR system for injection mould design based on ontology: A case study

Case-based reasoning (CBR) often shows significant promise for improving the effectiveness of design support in mould design, which is a domain strong in practice but poor in theory. However, existing CBR systems lack semantic understanding, which is important for intelligent knowledge retrieval in design support system. This hinders the application of CBR in injection mould design. In order to develop an intelligent CBR system and meet the need of design support for injection mould design, this paper integrates ontology technology into a CBR system by constructing domain ontology as case-base with a new method, in which two means of acquisition are combined, one is acquiring ontology from existing ontologies, the other from established engineering knowledge resources, and proposing a new semantic retrieval method as the first grade case retrieval. Numerical measurement is also employed as the second grade case retrieval, which adopts various methods to calculate different types of attribute values. A case is executed to illustrate the use of proposed CBR system, then a lot of experiments are organized to evaluate its performance and the result shows that the proposed approach outperforms existing CBR systems.