Simulation and knowledge based objects

A knowledge-based simulation model was developed by using a hypertext-like semi-object-oriented environment (SuperCard^TM). This environment enabled the building of a tightly coupled knowledge representation scheme and simulation algorithm. Our knowledge-based simulation model combines three different paradigms: production rules, an inference engine, and knowledge-based objects. The production rule paradigm is used as the basis for describing and coding the processes to be simulated. The knowledge-based objects are built as the active objects of the simulation. A knowledge-based object has a number of rules that support its behavior and an agenda that defines its goals. A knowledge-based object then proceeds in the simulation process (via the inference engine) until its goals are achieved. Since an inference engine with a knowledge base is built into the simulation process, such a system has the ability to reason about the behavior of the model. In addition, the knowledge-based simulation model has a friendly user interface. This enables changing the parameters of the simulation model without doing any programming. The inference engine in the knowledge-based simulation combines backward chaining, decision lattices, and links so as to produce an overall pattern of inference that is as efficient as possible.     

基于可拓规则的故障诊断专家系统推理机的研究

针对传统产生式规则在知识表示、匹配冲突等方面存在的局限,提出了一种将可拓规则用于故障诊断专家系统推理机的方法;该方法重点研究了可拓规则的匹配原理和可拓推理机算法思想,提出了匹配度计算方法并用来计算故障条件与规则前件的匹配度;根据研究表明,利用可拓规则进行推理,不仅在知识表示上比传统产生式规则推理有所提高,而且还解决了传统专家系统容易出现匹配冲突等问题;最后以AMU故障推理为例,说明可拓推理机具有推理速度快、效率高等优点,取得了较好的推理效果.     

卫星接收故障诊断专家系统的研究

文章分析了卫星接收系统的故障机理,研究了基于故障树的专家系统推理机的实现,提出了一种卫星接收故障诊断专家系统的设计思想及实现方法。推理过程以正向推理技术为主,应用二叉搜索树建立故障树,采用产生式表示法表示事实知识和规则知识,应用数据库作为知识库,构造了卫星接收故障诊断专家系统,用于对实时卫星接收过程的故障判断,指导操作员排疑解难。     

面向功能的表达系统HAR

HAR(Hypothesis Associative Representation)系统是一个面向功能的表达系统,由三部分组成:表达语言HARL、知识库管理子系统和推理机.HAR系统提供了三级描述和假设联想功能,把领域知识的表达和运用知识的推理控制策略紧密地结合在一起,实现对子问题求解的功能化表达.     

机车110V充电装置实时故障预警系统研究

110V充电装置作为机车的一个重要部分,其性能的好坏直接影响到机车的运行。本文以专家系统及故障树理论为基础,进行机车充电装置实时故障预警系统的研究。基于专家系统的故障诊断及预警方法进行分析研究后,将其应用到充电装置的针对预警中,建立了充电装置的故障树、知识获取、知识表示、故障预警推理机方法。然后,建立实时验证系统验证该理论分析及方法的可行性、有效性。     

电机自动装配线故障诊断模糊专家系统

为了对电机自动装配线上的故障进行正确有效的诊断，基于其工艺流程，运用模糊逻辑与专家系统知识，利用Visual C 以及Visual Foxpro作为编程工具，作者开发了用于装配线的故障诊断模糊专家系统（简称FES），该系统包括用户接口，知识库，推进机和历史记录数据库，采取模糊前向基于规则推理策略，并能根据可信度预测故障严重性。     

基于CAN总线的雷达装备BIT故障诊断专家系统研究

介绍了一种基于CAN总线的分散检测,集中诊断、显示的雷达BIT故障诊断系统,描述了其硬件组成、专家系统结构,知识表示和推理机制。通过在该雷达BIT中采用专家系统诊断方法,使得推理机与知识库分离,便于诊断知识库的扩充、维护,有效提高了系统BIT故障诊断能力。     

基于故障树的故障诊断IDSS的设计

将智能决策支持技术引入故障诊断,给出系统的总体设计,并详细阐述基于故障树的知识库和推理机的设计。采用面向对象基于框架、规则、元知识集成的广义故障树知识表示方法,同时给出了基于故障树最小割集和最小路集的故障快速推理算法,实现了故障的迅速诊断。     

电力系统故障预警推理引擎的设计与实现

针对电力系统中存在的设备故障及线路故障,该文在分析其具体类型和特点的基础上设计了将正向推理与反向推理相结合的组件化推理引擎,同时设计了与此引擎配套的知识库结构,并分别在．Net平台及MySQL上对引擎和知识库进行了实现。     

电力系统故障预警推理引擎的设计与实现

针对电力系统中存在的设备故障及线路故障,该文在分析其具体类型和特点的基础上设计了将正向推理与反向推理相结合的组件化推理引擎,同时设计了与此引擎配套的知识库结构,并分别在.Net平台及MySQL上对引擎和知识库进行了实现。     

海洋环境下武器效能评估系统的知识库构建

为了解决海洋环境下武器装备效能的评估问题,利用成熟的关系数据库技术提出了一种通用的知识表示方法,构建出海洋环境对武器效能影响评估系统的知识库。首先介绍了知识的两类来源,分析了效能评估知识的特点,表明传统的知识表示方法难以满足该评估系统的计算要求。然后详细地阐述了将规则、函数、算法、模型等统一到一个关系数据库中的通用关系知识表示方法,与一般知识表示进行比较,并根据其特点研究通用关系知识库的知识存储结构以及效能评估系统的推理机。最后给出基于框架知识的知识表示和推理实例。一个完善的通用关系知识库的构建将有助于解决海洋环境下武器装备效能的定量评估问题,为决策提供参考。     

Expert System Hardware for Fault Detection

This paper focuses upon the development of three new electronic architectures of inference engines as a part of a hardware expert system applied to very high-speed faults detection in industrial processes. The architecture of this expert system consists of an inference engine (a dedicated processor that is necessary due to the high-speed requirements and the repetitiveness of the operation), which uses a pattern-directed inference system; a fact base, which stores the status of the signals at each moment, and a static knowledge base, which contains the inference rules compiled from expert knowledge. A circuit for analyzing time is also presented. This allows time to be taken as another variable of the process and carries out a redundancy analysis simultaneously with the fault detection module.     

REX: Robotics expert systems building tool

REX, an expert systems development shell for robotics applications, is presented in the paper. The shell is intended for building expert systems in various domains of robotics, including design, planning, control, fault-detection, and navigation. REX's knowledge and data representation techniques include several standard techniques, like rules, frames, parameters, and variables, as well as certain techniques which are not commonly used by other expert systems building tools. These specific techniques include sensor data, status data, models and algorithms. The REX inference engine provides a means for reasoning with all of these knowledge and data types. The inference engine features both off-line and on-line modes of operation. REX has powerful interfaces for communicating both with the user and with other software modules in a complex application. Certain learning techniques are built into the shell. An example of applying REX for building an expert system in the domain of robot control is also presented. Finally, a discussion is provided on the relation between REX and second-generation expert systems.     

Acquiring and representing strategic knowledge in the diagnosis domain

Abstract: Techniques for acquiring and representing strategic knowledge for guiding diagnostic processes are presented. In a diagnostic expert system, strategic knowledge can be represented either by a specific knowledge base or it can be 'embedded' into the inference engine. We decided for the former; so that knowledge can be acquired or modified without affecting the problem solving paradigm. Strategic knowledge is acquired by expert interview in a straightforward way: on the basis of simple information provided by the expert, an internal sophisticated representation is automatically generated. The techniques are not restricted to a particular problem-solving paradigm or application. However, in order to prove the effectiveness of our approach, a problem solving paradigm is also presented. The paradigms adopted in diagnosis must face two problems: the selection of the 'right' hypothesis (fault) to pursue and the selection of the 'right' observation (measurement) to be executed. We present some criteria for selecting hypotheses and observations. Our proposal is suitable for domains where the measurements to localise the fault do not always provide certainty but only a 'degree of belief' about the presence of the fault. As a consequence, the problem of selecting the right measurement is solved by appropriate criteria and heuristic reasoning. Moreover, we do not consider 'right' as a predefined concept: actually, it is based on the information provided by the expert. So he can define this concept on the basis of his own judgment.     

为测试系统诊断设计并构造规则库和推理机

目前ＶＬＳＩ测试系统日趋复杂，要快速，精确地对它们进行诊断，不得不求助于专家系统，专家知识和解释专家知识并控制诊断进程的机制是专家系统的两大要素，它们又分别被称为规则库和推理机，针对这两大要素，本文阐述以下两方面的问题：（１）设计规则库的结构，（２）设计并构造用于ＶＬＳＩ测试系统诊断的推理机。     

基于OWL本体和SWRL规则的导弹智能故障诊断研究

针对复杂航空装备诊断知识缺乏、诊断效率低和知识共享性差等问题,以某型红外弹为例,提出一种基于OWL本体和SWRL规则的导弹智能故障诊断方法。首先以导弹FMECA结果作为知识源,通过基于ATML语法的OWL逻辑描述语言建立导弹本体模型,完成故障模式和故障原因本体之间的映射;其次采用语义网络规则语言SWRL描述知识库规则,建立本体知识单元之间类、属性和实例的对应关系,最后通过Racer推理机对导弹知识库进行故障诊断推理,获取故障诊断优先级顺序。推理结果表明,该方法能够解决复杂航空装备专家诊断系统中的知识表示困难、缺乏自动语义推理、重用共享性差等问题,获得最优的故障诊断路径的同时减少了故障排查步骤,从而实现了故障原因的快速定位,提高了复杂航空装备专家诊断系统的诊断效率和可靠性。     

汽车故障诊断专家系统关键技术的研究与发展

在收集整理大量国内外相关研究文献的基础上,针对知识获取方法、知识表示方法及推理策略等这些关键技术相应的解决方法进行了分析,包括对传统方法的改进,如故障规则的自动获取、组合的知识表示方法和推理方法的多样化等,以及新理论新技术的应用,如基于案例的专家系统、基于模糊的专家系统、基于神经网络的专家系统以及基于行为的专家系统等,并提出智能化、网络化和集成化是未来故障诊断专家系统的发展方向。     

基于CLIPS的卫星任务规划专家系统设计

本文提出了基于CLIPS的卫星任务规划专家系统的设计方法,详细分析了系统的结构和功能,重点讨论了中文产生式系统的BNF范式、基于上下文的推理机制和集合运算符。中文产生式系统的BNF范式基于CLIPS标准BNF范式定义,并依据BNF范式进行规则表示和规则自定义获取;推理机采用上下文限制的规则控制策略,依据不同的上下文加载相关的事实和规则,提高推理机的运行效率;利用规则中的对象逻辑子式进行了集合运算符的设计,并对极值运算符、属性差值运算符和均值运算符等三类集合运算符进行了探讨。该系统解决了卫星任务规划中知识表示和知识获取问题,提高了卫星任务规划推理效率,为卫星任务规划人员提供有效的辅助决策功能。     

A Knowledge-Based Approach to Effective Document Retrieval

This paper presents a knowledge-based approach to effective document retrieval. This approach is based on a dual document model that consists of a document type hierarchy and a folder organization. A predicate-based document query language is proposed to enable users to precisely and accurately specify the search criteria and their knowledge about the documents to be retrieved. A guided search tool is developed as an intelligent natural language oriented user interface to assist users formulating queries. Supported by an intelligent question generator, an inference engine, a question base, and a predicate-based query composer, the guided search collects the most important information known to the user to retrieve the documents that satisfy users' particular interests. A knowledge-based query processing and search engine is devised as the core component in this approach. Algorithms are developed for the search engine to effectively and efficiently retrieve the documents that match the query.