智能电脑刺绣集成环境ICEE

运用图像处理技术、超媒体技术和专家系统、基于实例推理等人工智能技术,设计并实现了智能电脑刺绣集成环境ＩＣＥＥ,它将刺绣编程、图案装配和刺绣知识咨询等系统集成在一起,实现了图案设计、输入、编程、优化和数据转换的一体和自动化能产生高质量的绣品数据样板,提高电脑刺绣的效率。     

基于知识的刺绣编程系统

介绍了一个基于知识刺绣编程系统。描术字该系统的结构,数据结构和主要实现技术,该系统融合了图象处理,人工智能和计算机辅助设计等多种技术,引入了丰富的刺绣知识、编针规则和有效的推理机制,具有自动推理编针能力,实现了图案处理,编针,优化和数据转换的一体化与自动化,提高了刺绣编程质量和效率。     

离散动态贝叶斯网络推理的编程计算算法

离散动态贝叶斯网络是对时间序列进行建模和推理的重要工具,具有广泛的建模应用价值,但是其推理算法还有待进一步完善。针对构离散动态贝叶斯网络的推理算法难以理解、编程计算难、推理速度慢的问题,给出了实现离散动态贝叶斯推理算法的数据结构,推导了进行计算机编程计算的推理算法和编程步骤,并通过实例进行了算理验证。     

知识存储与诊断专家系统在低温系统中的应用

为实现EAST低温系统的智能故障诊断,提出一种基于故障树的低温系统知识存储与诊断专家系统。根据故障树模型的结构,设计知识在关系型数据库中的分级存储方式。应用故障树分析法进行故障建模与诊断推理,结合可靠性因子辅助不确定性推理,并以实例说明推理算法。模拟诊断结果表明,该系统具有友好的人机接口,运行稳定且诊断结果及时可靠。     

轴类件耦合神经网络实例推理CAPP索引模型的研究

本文提出了一种基于神经网络的工艺设计实例推理索引模型。与现存大多数实例推理系统不同该方法用神经网络实现实例的动态分类和索引。实例层次分类的三层结构和基于特征的聚类模板概念，为实现基于符号处理的实例推理求解模式向基于神经计算的模式识别求解模式映射提供了条件。该方法的优点在于实例的高速、有效检索，知识获取的简化以及基于神经网络的检索算法的鲁棒性。     

基于矩阵运算的Petri网反向推理算法

通过对加权模糊Petri网模型和反向推理算法的研究,建立了加权模糊Petri网的关联Petri网推理模型。在此基础上,提出了基于矩阵运算的反向推理算法。通过实例对算法进行了检验,结果表明该算法具有解决复杂问题的并行推理能力、推理效率高、推理过程简单、容易实现。     

IHSMT中的实例优化策略

在基于实例的机器翻译方法中，通常采用双语句子实例的形式，但由于自然语言表达的无限多样性，使得这种存储粒度过大的句子级翻译实例的利用率较低；而在基于规则的机器翻译方法中，规则和词典具有一定的抽象性，其重复利用率高，但其中存在大量的歧义，提出了一种IHSMT中的实例自动优化算法，该算法引入了实例粒度的概念，根据规则和实例使用率，将粒度大的实例分解为较小的单元，并建立相应的词典，以提高实例的使用频率和检索效率，同时在翻译过程中，根据用户修改的统计和上下文信息，对粒度小并且有歧义的实例进行合并，以提高翻译质量和推理效率，从而使实例粒度更加合理，存储结构更优化，系统的效率更高。     

产品的相似性推理:一种描述逻辑的方法

采用产品本体理论对设计进行本体标注，得到对应产品设计的形式化概念，在此基础上通过本体的推理——基于描述逻辑的知识推理，对产品进行分类．文中分别就设计的相似性和基于实例的设计中实例间的相似性推理提出了明确的算法，并通过实例进行了说明．     

基于实例的客运站股道运用实时决策推理方法*

客运站股道运用实时决策问题是一个半结构化问题,实时决策推理研究为解决特殊情况下铁路客运站实时决策问题提供理论基础,具有十分重要的现实意义。采用物元理论,构建基于实例的实时决策推理物元模型,将实时决策物元分为列车物元、方案物元和推理物元,结合车站调度员解决特殊情况下股道运用实时决策的思维过程,采用优度评价法和基于双空间模型的归纳学习法,设计基于实例的实时决策推理算法和自学规则算法,提出铁路客运站股道运用实时决策推理方法,用于制订特殊情况下的实时决策方案。实例分析结果表明,所提出的模型与算法能有效解决特殊情况下客运站股道运用实时决策问题,提高了股道运用实时决策的智能化水平。     

基因表达式编程在软件可靠性建模中的应用

基因表达式编程是一种基于遗传算法和遗传编程的新型机器学习技术,其具有更为优秀的数据挖掘能力,已被成功应用于函数发现领域。提出一种基于基因表达式编程的非参软件可靠性建模方法,该方法将基因表达式编程算法中的若干关键步骤(如初始种群函数集、适应度函数、终止条件等)与软件可靠性建模的若干重要特征相融合,在失效数据集上进行训练,从而获得基于基因表达式编程算法的非参软件可靠性模型。在若干组真实失效数据集上,将所提出的模型与若干典型的基于人工神经网络以及遗传编程的非参软件可靠性模型进行对比实例研究。实例结果表明,基因表达式编程算法的非参软件可靠性模型具有更为显著的模型拟合与预计性能。     

基于案例推理的谈判支持系统的研究

通过对谈判案例的表达、检索、复用、评价、适配和学习的研究,基于案例推理的谈判支持系统解决了谈判者谈判相关知识缺乏的问题;在谈判案例表达方法中提出了属性分类方法;采用改进的最近相邻法进行谈判案例适配,以获得更相近的谈判历史案例;通过对基于案例推理的谈判机制分析,构建了基于案例推理谈判支持系统的体系结构,并详细设计了谈判系统的功能.最后,通过一个采购谈判方案验证了基于案例推理谈判支持系统的实用性.     

A case-based reasoning system development for statistical process control: Case representation and retrieval

Statistical process control (SPC) is a sub-area of statistical quality control. Considering the successful results of the SPC applications in various manufacturing and service industries, this field has attracted a large number of experts. Despite the development of knowledge in this field, it is hard to find a comprehensive perspective or model covering such a broad area and most studies related to SPC have focused only on a limited part of this knowledge area. According to many implemented cases in statistical process control, case-based reasoning (CBR) systems have been used in this study for developing of a knowledge-based system (KBS) for SPC to organize this knowledge area. Case representation and retrieval play an important role to implement a CBR system. Thus, a format for representing cases of SPC and the similarity measures for case retrieval are proposed in this paper.     

基于文本案例的推理技术综述

首先介绍了CBR(Case-based reasoning)推理中一个重要分支--基于文本案例的推理技术TCBR(Textual CBR)的产生背景.然后详细介绍了TCBR推理时采用的结构--实例提取网CRN(Case Retrieval Nets),描述案例的知识单元--信息实体的概念和特性,以及TCBR中如何进行实例提取、改编和学习.最后,对TCBR的改进研究、代表项目以及未来的发展方向作了概括性说明.     

Research of new strategies for improving CBR system

Case based reasoning (CBR), as an important AI technology, has gained popularity for its unique means of problem solving, which solves a new problem by remembering previous similar situations and reusing knowledge from the solutions to these situations. To construct a CBR system, two key issues have to be considered: one is feature selection, through which important features are extracted from the whole experience case and make up a case; the other is case retrieval, through which most appropriate case is retrieved for reuse. In order to further improve the accuracy of CBR system, this paper proposes a new feature selection method called Calculating Differences based on Growing Hierarchical Self Organizing Map clustering (CD-GHSOM) and a new case retrieval method called Growing Hierarchical Self Organizing Map based Case Retrieval (GHSOM-CR). Lots of experiments are implemented to validate the effectiveness of the proposed methods by comparing them with other recent researches.     

基于范例推理的交通事故智能处理系统

介绍了一个基于范例推理的交通事故智能处理系统。首先给出了基于CBR的交通事故智能处理系统的总体框架。之后,对该系统中的核心部分：范例搜索、基于范例推理的综合推理模型、人机智能系统分别进行了讨论。     

Hierarchical case-based reasoning integrating case-based anddecompositional problem-solving techniques for plant-control softwaredesign

Case based reasoning (CBR) is an artificial intelligence technique that emphasises the role of past experience during future problem solving. New problems are solved by retrieving and adapting the solutions to similar problems, solutions that have been stored and indexed for future reuse as cases in a case-base. The power of CBR is severely curtailed if problem solving is limited to the retrieval and adaptation of a single case, so most CBR systems dealing with complex problem solving tasks have to use multiple cases. The paper describes and evaluates the technique of hierarchical case based reasoning, which allows complex problems to be solved by reusing multiple cases at various levels of abstraction. The technique is described in the context of Deja Vu, a CBR system aimed at automating plant-control software design     

Hybrid similarity measure for case retrieval in CBR and its application to emergency response towards gas explosion

Case retrieval is a primary step in case-based reasoning (CBR). It is important to measure the similarity between each historical case and the target case during the case retrieval process. In recent years, some methods for similarity measure with multiple formats of attribute values can be found in the practical CBR applications, but the in-depth study is still lacking. The objective of this paper is to develop a new method for hybrid similarity measure with five formats of attribute values: crisp symbols, crisp numbers, interval numbers, fuzzy linguistic variables and random variables. First, for each format of the attribute values, the calculation formula to measure the attribute similarity is presented. Then, the method for measuring hybrid similarity between each historical case and the target case is given by aggregating attribute similarities using the simple additive weighting method, and the proper historical case(s) can be retrieved according to the obtained hybrid similarities afterwards. Finally, a case study in the field of emergency response towards gas explosion is introduced to illustrate the use of the proposed method.     

Case-based reasoning classifier based on learning pseudo metric retrieval

In case-based reasoning (CBR) classification systems, the similarity metrics play a key role and directly affect the system's performance. Based on our previous work on the learning pseudo metrics (LPM), we propose a case-based reasoning method for pattern classification, where the widely used Euclidean distance is replaced by the LPM to measure the closeness between the target case and each source case. The same type of case as the target case can be retrieved and the category of the target case can be defined by using the majority of reuse principle. Experimental results over some benchmark datasets and a fault diagnosis of the Tennessee-Eastman (TE) process demonstrate that the proposed reasoning techniques in this paper can effectively improve the classification accuracy, and the LPM-based retrieval method can substantially improve the quality and learning ability of CBR classifiers.     

一种用于CBR推理机的案例学习算法研究*

案例学习是CBR(Case-Based Reasoning)推理机的重要环节,但由于案例的多样性以及对领域的依赖性,导致CBR系统中案例自动生成困难的问题。针对这一问题,本文提出将seq2seq(Sequence-to-Sequence)模型用于案例学习,通过seq2seq模型自动生成案例,引入attention机制,提高seq2seq模型生成案例的效果,并利用潜在语义分析LSA(Latent Semantic Analysis)对网络爬取语料库进行筛选,利用过滤后的语料库对模型进行训练,提出一种基于三元组的评估方法,对生成案例进行评估和存储,从而实现CBR推理机的自主学习。最后将改进的案例学习系统应用到实际的智能机器人上进行验证,测试结果表明该方法具有可行性,且能够有效提高机器人的智能性及易用性。     

分布式环境下基于语义相似的案例检索

分布式环境下的异构案例表达制约了案例检索过程中案例属性之间的可比性，进而成为分布式环境下案例推理系统成败的一个关键问题。该文提出基于语义相似的案例检索，通过利用Ontology技术来理解案例属性的内在含义，在此基础上定义并计算属性之间的相似程度。对原型系统的初步测试证明了基于语义相似的案例检索有效性。