基于关系数据库的脑血管疾病辅助诊断专家系统

基于关系数据库和VisualBasic6.0设计实现了脑血管疾病辅助诊断专家系统.该系统利用产生式规则表达知识,引入可信度进行不精确推理,在缺乏各项生化检查以及影像学检查的情况下,通过症状、体征等就能为患者给出迅速基本的诊断.     

基于产生式规则的乔灌木识别推理算法研究

为提升公众林业知识水平,促进树种知识的推广,以北京市乔灌木枝叶检索表中的枝叶检索知识为基础,采集乔灌木枝叶特征图片、树种知识及树种图片,使用产生式规则表示法对枝叶检索知识进行表达和组织,构建了枝叶检索知识的链式双亲表示模型,建立了乔灌木识别知识库,设计了乔灌木识别推理算法。在此基础上,研建了北京市乔灌木识别专家系统,实现了专家知识的存储、乔灌木识别推理算法以及乔灌木树种的识别。运行实例表明,采用产生式规则知识表示法构建的乔灌木识别推理算法,能够实现乔灌木树种的准确识别。     

基于人工神经网络的葡萄病害诊断专家系统

设计了一种基于人工神经网络的葡萄病害诊断专家系统。以常见的18种主要的葡萄病害为研究对象,将专家知识转换为诊断规则,并作为学习样本输入神经网络进行训练,形成人工神经网络推理机。同时,采用知识库、规则推理和人工神经网络推理相结合的系统结构来优化专家系统,在提高专家系统自学能力的同时也提高了系统的响应速度。采用C#、Matlab和.NET技术混合编程实现专家系统,实验结果表明该系统有较高的诊断准确率并能稳定运行。该系统在Web上运行,更有利于系统的推广应用。     

An evolutionary‐based adaptive neuro‐fuzzy inference system for intelligent short‐term load forecasting

The continuing growth in size and complexity of electric power systems requires the development of applicable load forecasting models to estimate the future electrical energy demands accurately. This paper presents a novel load forecasting approach called genetic‐based adaptive neuro‐fuzzy inference system (GBANFIS) to construct short‐term load forecasting expert systems and controllers. At the first stage, all records of data are searched by a novel genetic algorithm (GA) to find the most suitable feature of inputs to construct the model. Then, determined inputs are fed into the adaptive neuro‐fuzzy inference system to evolve the initial knowledge‐base of the expert system. Finally, the initial knowledge‐base is searched by another robust GA to induce a better cooperation among the rules by rule weight derivation and rule selection mechanisms. We show the superiority and applicability of our approach by applying it to the Iranian monthly electrical energy demand problem and comparing it with the most frequently adopted approaches in this field. Results indicate that GBANFIS outperforms its rival approaches and is a promising tool for dealing with short‐term load forecasting problems.     

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。     

基于专家系统的协同设计工作流管理系统研究

本文提出了一种支持协同产品设计的以规则为基础的工作流管理系统模型架构,其中以专家系统的推论引擎作为工作流程管理系统的推进机制,利用推论引擎的知识库存储流程的事实与规则,以各任务的输出结果、任务间的关联性并搭配预先定义好的流程规则作为推论的依据,讨论了系统的架构设计、关键技术及系统实现,该系统为复杂的工作流程提供了一种新的管理及控制模式。     

基于模糊神经网络的畜禽疾病诊断系统的研究

文中结合模糊数学的隶属度、知识的表示、知识的获取、知识的推理、神经网络和统计方法中的最大似然估计理论建立了一个畜禽疾病诊断的医疗专家系统模型，通过把推理规则模糊数值化的值作为神经网络的权重，利用神经网络模拟计算对畜禽所患疾病作出正确的诊断。本诊断系统以指导兽医新手为目的，诊断结果的准确率达90％以上，具有较好的实际应用价值。     

基于Web的非精确反向推理专家系统研究与实现

介绍了一个基于Web的精确反向推理专家系统原型,通过对其增加非精确推理功能,完成了对此原型的改进,使其能进行非精确分类或诊断。提出在推理过程中对所使用的规则、条件等信息的收集及处理算法,并给出一个推理过程分析实例,论述了对原型改进的基本步骤,同时也提供了一个知识库例子。探讨基于Web的专家系统的应用实践,对于推动专家系统在我国的应用具有现实意义。     

分布式协同中医诊断系统的设计

利用“分布式协同专家系统开发工具ＢＩＴＡＩ－ＤＥＳＴ”,采用中医咳嗽诊断及胸痹诊断的专家知识,建立分布式协同的知识表示体系,分别构造各协同目标的规则及事实,生成一个具有实用意义的多智能体协同求解的中医诊断专家系统。     

Design and simulation of a parallel inference machine architecture for rule based systems

This paper presents an architecture of the inference machine for a rule based expert system. The paper, structured around the concept of “inference flow graphs”, is aimed at incorporating parallelism in antecedent matching to find out the firable rules as well as firing more than one rule simultaneously, whenever required. Through this architecture, the number of comparisons required during the antecedent matching phase, is significantly reduced. The flow of inferencing can also proceed in a pipelined manner resulting in faster inferences.     

Adaptive fuzzy PI controller with shifted control singletons

It is well known the fact that the design of a fuzzy control system is based on the human expert experience and control engineer knowledge regarding the controlled plant behavior. As a direct consequence, a fuzzy control system can be considered as belonging to the class of intelligent expert systems. The tuning procedure of a fuzzy controller represents a quite difficult and meticulous task, being based on prior data regarding good knowledge of the controlled plant. The complexity of the tuning procedure increases with the number of the fuzzy linguistic variables and, consequently, of the fuzzy inference rules and thus, the tuning process becomes more difficult. The paper presents a new design strategy for such expert fuzzy system, which improves their performance without increasing the number of fuzzy linguistic variables. The novelty consists in extending the classic structure of the fuzzy inference core with an intelligent module, which tunes one of the control singletons, providing a significant simplification of the design and implementation procedure. The proposed strategy implements a logical, not physical, supplementation of the linguistic terms associated to the controller output. Therefore, a fuzzy rules set with a reduced number of linguistic terms is used to implement the expert control system. This logical supplementation is based on an intelligent algorithm which performs a shifting of only one of the control singletons (the singleton associated to the SMALL_ linguistic variable), its value becoming variable, a fact that allows an accurate control and a better performance for the expert control system. The logic of this intelligent algorithm is to initially provide a high controller output, followed by a slowdown of the control signal near to the operating set point. The main advantage of the proposed expert control strategy is its simplicity: a reduced number of linguistic terms, combined with an intelligent tuning of a single parameter, can provide results as accurate as other more complex available solutions involving tuning of several parameters (well described by the technical literature). Also, a simplification of the preliminary off-line tuning procedure is performed by using a reduced set of fuzzy rules. The generality of the proposed expert control strategy allows its use for any other controlled process.     

Fuzzy expert system for predicting pathological stage of prostate cancer

Prostate cancer is the second most common cancer among men, responsible for the loss of half a million lives each year worldwide, according to the World Health Organization. In prostate cancer, definitive therapy such as radical prostatectomy, is more effective when the cancer is organ-confined. The aim of this study is to investigate the performance of some fuzzy expert systems in the classification of patients with confined or non-confined cancer. To deal with the intrinsic uncertainty about the variables utilized to predict cancer stage, the developed approach is based on Fuzzy Set Theory. A fuzzy expert system was developed with the fuzzy rules and membership functions tuned by a genetic algorithm. As a result, the utilized approach reached better precision taking into account some correlated studies.     

基于虚拟仪器技术的故障诊断专家系统的实现

随着计算机技术的发展,智能技术在测试领域显得越来越重要,在传统的测试系统中使用专家系统技术成为测试系统的发展趋势;文章通过对专家系统原理的介绍,提出了将专家系统技术引入测试领域的可行性,并分别介绍了基于虚拟仪器的测试系统软件设计方法和基于规则的专家系统软件设计方法,并给出了故障诊断专家系统的实现方法和实时故障专家系统的设计方案;经使用后,认为专家系统技术将有助于智能化故障诊断的实现,并且可提高测试技术的智能化程度。     

Designing fuzzy inference systems from data: Aninterpretability-oriented review

Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation and system optimization. Rule generation leads to a basic system with a given space partitioning and the corresponding set of rules. System optimization can be done at various levels. Variable selection can be an overall selection or it can be managed rule by rule. Rule base optimization aims to select the most useful rules and to optimize rule conclusions. Space partitioning can be improved by adding or removing fuzzy sets and by tuning membership function parameters. Structure optimization is of a major importance: selecting variables, reducing the rule base and optimizing the number of fuzzy sets. Over the years, many methods have become available for designing FIS from data. Their efficiency is usually characterized by a numerical performance index. However, for human-computer cooperation another criterion is needed: the rule interpretability. An implicit assumption states that fuzzy rules are by nature easy to be interpreted. This could be wrong when dealing with complex multivariable systems or when the generated partitioning is meaningless for experts. The paper analyzes the main methods for automatic rule generation and structure optimization. They are grouped into several families and compared according to the rule interpretability criterion. For this purpose, three conditions for a set of rules to be interpretable are defined     

基于本体的应急救援知识库构建及其应用

针对专家系统在应急救援领域应用中存在的知识表示及推理等问题,采用基于本体的知识表示方法与基于Jena的规则推理引擎,参考简单知识工程方法论与Jena规则语法建立一个高速公路应急救援本体与推理规则,实现本体知识库的推理。将该知识库应用于高速公路应急救援系统中,结果表明其具备解决实际问题的能力;有利于领域知识的共享与重用;促进了专家系统在高速公路应急救援领域的发展。     

An example of expert system on numerical modelling system in coastal processes

With the advent of artificial intelligence technology as well as the widespread popularity of desktop microcomputers in recent years, integration of this new technology with the traditional numerical modelling system becomes a current trend in order to solve various engineering problems. It renders a more intelligent and user-friendly system on the problem domain. In this paper, a knowledge-based expert system on numerical modelling system for coastal water processes is delineated. Expert system application, as a key branch of artificial intelligence technology, is integrated with traditional numerical modelling for simulating flow and water quality phenomenon in coastal waters. The knowledge bases are classified into five major types, namely, a variety of models, relations between various model parameters and real physical conditions, feasible options of model parameters, question base as a user-interface directing the user to depict the actual physical conditions, and the rules of inference deducing the feasible choice of model and its parameters. A hybrid expert system shell, Visual Rule Studio, is employed as an ActiveX Designer under Microsoft Visual Basic environment because it combines the advantages of both production rules and object-oriented programming technology. Both forward chaining and backward chaining are used collectively during the inference process, which is mainly driven by premises and conditions with the highest factors of confidence. The inference engine will drive the decision tree to explore the most probable option of numerical model and parameters matching the real problem specifications. It is shown that the application and integration of the knowledge-based expert system technology into numerical modelling for coastal processes can provide substantial assistance to novice users for selection of numerical model as well as parameters.     

掘进机液压系统故障诊断专家系统的研究

文章对专家系统技术在设备故障诊断方面的应用进行了研究与探讨。建立了一个煤巷掘进机液压故障诊断专家系统，介绍了系统的结构、知识表示、推理机制及知识获得等。     

Application of an expert knowledge-based system to the semiconductor thin film device coating process

This paper briefly describes an expert knowledge-based system which assists and diagnoses in the thin film device coating process. The system gains knowledge from qualitative and experience judgement of the operation in supervisory work and self experience. An explanation is given of the expert knowledge-based system and an example is given of the user interface. Various problems in the production process itself which experts in the laboratory find difficult to solve are indicated. A knowledge-based representation scheme involving rules is presented as well. The system currently under development would be a flexible time-saving and practical decision support system.     

Orthogonal Fuzzy Rule-Based Systems: Selection of Optimum Rules

In this paper, the concept of orthogonal fuzzy rule-based systems is introduced. Orthogonal rules are an extension to the definition of orthogonal vectors when the vectors are vectors of membership functions in the antecedent part of rules. The number and combination of rules in a fuzzy rule-based system will be optimised by applying orthogonal rules. The number of rules, and subsequently the complexity of the fuzzy rule-based systems, are directly associated with the number of input variables and distinguishable membership functions for each individual input variable. A subset of rules can be used if it is known which subset provides closer behaviour to the case when all rules are used. Orthogonal fuzzy rule-based systems are proposed as a judgment as to whether the optimal rules are selected. The application of orthogonal fuzzy rules becomes essential when fuzzy rule-based systems containing many inputs are used. An illustrative example is presented to create a model for the solder paste printing stage of surface mount tech-nology.     

关系数据库在专家系统中的应用

在传统的专家系统中，对推理机工作过程的控制是程序实现的。本文介绍了一个用数据库技术引导推理过程的专家系统－精神分裂症诊断专家系统（ＥＳＳＤ）的推理控制策略。在这个专家系统中，作者将有关推理过程的控制信息用关系数据库技术进行存储和管理，达到了将控制策略数据化的目的。将数据库技术引入到专家系统的推进控制机制中可以使控制策略比较容易调整，且而可以大大压缩程序篇幅，显著提高系统运行效率，为专家系统的结构设