知识网格环境下基于本体的CBR诊断系统架构研究

提出知识网格环境下基于本体的分布式多案例推理系统架构。该架构引入基于本体的案例推理服务器和任务-方法本体,实现了过程性知识和陈述性知识分离,有效地解决了案例推理重用问题;在案例推理服务器中共享本体约束指导下,各案例库可以灵活地表达各自领域知识,实现知识集成,解决了传统案例推理系统知识局限性问题,实现了各企事业单位之间知识共享和重用。该架构是开放的并采用Web Service技术构建,能够实现网格异构环境下系统集成,具有很强的扩展性。     

基于范例推理的税收案例分析系统设计

基于范例推理技术是专家系统实用而成熟的技术。对税收案例分析而言,其应用在实用性方面优于基于规则的系统。提出基于范例推理技术和应用XML来表示和搜索范例,应用数据仓库构建税收案例分析系统,详细介绍了系统的基于范例推理流程、相似性度量函数、范例相似匹配方法及范例维护的设计思路和实现步骤。利用基于范例推理技术,大大提高了系统的“智能”性和实际功能,在实际应用中产生了较好的经济效益和社会效益。     

知识管理系统中的CBR技术研究

基于案例的推理技术尝试在计算机上将叙述能力与知识整理进行结合,在为知识管理系统的实现提供了基本的技术保障的同时,也拓宽了推理技术的应用领域。首先讨论了基于案例的推理的一些基本问题,然后借助一个实例探讨了CBR技术在知识管理系统中的应用,并提出了进一步的研究方向。     

分布式多层应用系统实证研究

探讨分布式应用系统基本理论以及特点,阐述了使用Delphi实现分布式系统应用的相关技术,结合实际案例,实现了基于分布式应用系统和浏览器相结合的成本信息系统,并进一步探讨了构建这种应用系统需要注意的问题。     

案例推理在果树病虫害诊断与防治专家系统的研究

根据果树病虫害诊断与防治的特点和要求,将案例推理技术引入到果树病虫害诊断与防治专家系统设计中。阐述了案例推理概念和工作过程,在具体应用中提出了案例知识表示、两层的案例库组织结构和检索推理机制,得出基于案例推理果树病虫害诊断与防治专家系统实现算法流程。该系统在实际应用中获得了较好的效果。     

基于案例推理的供应商选择决策支持系统研究

在介绍了基于案例推理方法的基本原理基础之上,分析了基于案例推理技术的供应商选择决策支持系统的工作原理、框架结构及功能;重点论述了基于案例推理的供应商选择决策支持系统中的一些关键步骤,并结合实例给出了基于案例推理的供应商选择与评价方法,用来验证基于案例推理技术在供应商选择决策支持系统中应用的可行性和有效性,为企业供应商选择决策提供了一个系统模型。     

基于案例和模糊推理的农业虫害专家系统研究

为了满足实.际虫害诊断问题对专家系统的要求,根据虫害特征诊断的现实特点和要求,首次将模糊技术和案例推理相融合,引入到虫害诊断专家系统的设计中.阐述了模糊案例推理的知识表示,给出了模糊案例推理技术的推理过程,提出了基于案例与模糊推理的虫害诊断推理机制.对二者的结合方式做了阐述,给出了专家系统的整体实现结构,并时各个模块的实现方法进行了详尽的阐述,最终以一个实例演示了实例诊断的流程.     

基于案例推理的土石坝病险智能诊断系统

近年来基于案例推理已受到广泛关注，将基于案例推理技术应用到土石坝病险诊断系统中，为土石坝病险智能化诊断提供了一条新的途径。该文针对土石坝病险诊断的案例特点，设计了案例的集成框架表示和分层模糊检索策略，并提出了诊断系统的实现方案。     

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.     

案例推理在果树病虫害诊断与防治专家系统的研究

根据果树病虫害诊断与防治的特点和要求，将案例推理技术引入到果树病虫害诊断与防治专家系统设计中。阐述了案例推理概念和工作过程，在具体应用中提出了案例知识表示、两层的案例库组织结构和检索推理机制，得出基于案例推理果树病虫害诊断与防治专家系统实现算法流程。该系统在实际应用中获得了较好的效果。     

Integrating diverse information resources in a case-based design environment

The success of case-based design aids depends both on the case-based reasoning processes they apply and on effectively integrating those processes into the larger task context: on making the case-based reasoning component present case information at the right time and in the right way, on exploiting additional information resources as needed to supplement the case library and to guide case application, on capturing useful information from current reasoning and providing it to up- and down-stream designers, and on unobtrusively learning new cases during the design process. This article presents a set of principles and techniques for integrated case-based design support systems and illustrates their application through a case study of the Stamping Advisor, a system to support feasibility analysis for sheet metal automotive parts.     

基于不精确信息实例检索模型的研究

传统实例检索模型缺乏对不确定环境中不精确信息的适应性。采用构造因果网络的分阶段实例检索模型,则可以有效地处理实例检索中的不精确性,并能提高基于实例推理系统的性能。     

RespiDiag: A Case-Based Reasoning System for the Diagnosis of Chronic Obstructive Pulmonary Disease

In this paper a decision support system for the diagnosis of a very serious respiratory disease caused by tobacco named the chronic obstructive pulmonary disease is presented. The system is based on case-based reasoning principles and gathers the experience of experts of the pneumology department of Dorban Hospital (Annaba, Algeria). A critical issue about the case base is that some values of the features are missing in most cases. Five approaches for managing this problem of missing data are proposed. Three of them allow evaluating the similarity despite the missing information. The two other approaches fill the voids with plausible values using a statistical method and the principles of case-based reasoning itself.     

Developing an energy-saving and case-based reasoning information agent with Web service and ontology techniques

Web service and ontology techniques are presented herein for supporting an energy-saving and case-based reasoning information agent. The proposed system is the first energy-saving and case-based reasoning information agent with Web service and ontology techniques in a cloud environment; the proposed architecture is also the first multi-agent structure of an energy-saving information system in a practical environment. Not only can it explore related technologies to establish a Web service platform, but it can also study how to construct cloud interactive diagrams to employ Web service techniques for extensively and seamlessly integrating energy-saving and a case-based reasoning information agent on the Internet. The complete in depth system development, display, and corresponding experiments and comparisons show that the research results not only attest to the feasibility of the proposed architecture, but are also highly successful; on average, 40% of the data queries can be answered by the proposed system, and its rate of correct data solutions is around 85.1%, leaving about 60% of the queries for the backend system to take care of, which can effectively alleviate the overloading problem usually associated with a backend server. Finally, the system is put into a practical environment; after 8 months of experiments, the total energy-saving is 22.44%.     

MBNR: Case-Based Reasoning with Local Feature Weighting by Neural Network

Our aim is to build an integrated learning framework of neural network and case-based reasoning. The main idea is that feature weights for case-based reasoning can be evaluated by neural networks. In this paper, we propose MBNR (Memory-Based Neural Reasoning), case-based reasoning with local feature weighting by neural network. In our method, the neural network guides the case-based reasoning by providing case-specific weights to the learning process. We developed a learning algorithm to train the neural network to learn the case-specific local weighting patterns for case-based reasoning. We showed the performance of our learning system using four datasets.     

Experience,introspection and expertise: Learning to refine the case-based reasoning process

The case-based reasoning paradigm models how reuse of stored experiences contributes to expertise. In a case-based problem-solver, new problems are solved by retrieving stored information about previous problem-solving episodes and adapting it to suggest solutions to the new problems. The results are then themselves added to the reasoner's memory in new cases for future use. Despite this emphasis on learning from experience, however, experience generally plays a minimal role in models of how the case-based reasoning process is itself performed. Case-based reasoning systems generally do not refine the methods they use to retrieve or adapt prior cases, instead relying on static pre-defined procedures. The thesis of this article is that learning from experience can play a key role in building expertise by refining the case-based reasoning process itself. To support that view and to illustrate the practicality of learning to refine case-based reasoning, this article presents ongoing research into using introspective reasoning about the case-based reasoning process to increase expertise at retrieving and adapting stored cases.     

基于混合推理机制的故障诊断专家系统

多数故障诊断专家系统采用单一的推理机制,或者基于规则的推理,或者基于事例的推理。而这两种推理机制都各有优缺点,采用单一推理机制会造成诊断的不准确性。论文将基于规则的推理和基于事例的推理相结合,设计了混合推理机制。在此基础上,论文设计了一个既有专家知识库,又有故障事例库,具有自学习能力的故障诊断专家系统(AFDES)。实验结果表明,论文设计的混合推理机制是比较有效的。     

Toward a user-oriented recommendation system for real estate websites

The Internet has become a significant transaction platform for the real estate industry. However, use of the Internet does not benefit homebuyers in terms of search time, flexibility, and intuitive results. While it does encourage buyers to search more intensively, and discover and visit more properties, it also wastes more time and energy. To improve the efficiency of real estate searches, we developed an online homebuyer’s search program, based on an investigation of search behaviors, and implement a user-oriented recommendation system for real estate websites via a combination of case-based reasoning and an ontological structure. An ontological structure is employed to improve information management efficiency while case-based reasoning improves recommendation accuracy. A user test demonstrates the effectiveness of the proposed system and validates the findings of this study. The limitations of the current study are also discussed for future research and applications.