专家系统方法选择负载型催化剂的溶液浸渍制备的参数

负载型催化剂的浸渍制备涉及到大量的经验规律,这就使得其条件参数的选择成为一个比较复杂的不确定性问题,借助于专家系统方法可望解决此类问题。本文在单组分浸渍系统的基础上,形成了一个多组分浸渍制备的专家系统原型IACES—Ⅱ,该系统由Prolog和Fortran语言编码,具备处理数值和非数值信息的功能。在IACES-Ⅱ系统中,采用集总的方法将浸渍参数的优化选择问题分解为若干子问题,并采用“规则架+规则体”这种基本结构来形成知识库的整体结构。然后,通过基于目标的推理给出如下浸渍参数:(1)吸附形式,(2)离子形式,(3)浸渍体系稳定的pH值范围,(4)竞争吸附剂,(5)操作参数(初始浓度,载体用量和溶液体积之比,浸渍时间等)。     

旋转机械振动故障诊断专家系统

针对旋转机械故障诊断智能化程度不高的现状,采用可视化语言LabVIEW作为故障诊断专家系统前端开发平台,采用MySQL作为系统后台数据库,开发了一个基于产生式规则表示知识的旋转机械故障诊断专家系统。介绍了该系统的总体结构、知识库、推理机的设计。最后给出该专家系统的实例验证,证明了该方法的实用性。     

故障诊断专家系统的可拓知识表示和匹配研究

知识表示和匹配是设计专家系统的核心问题;首先通过引入基元理论,实现了产生式、语义网络、框架和案例的基元表示,建立了故障诊断专家系统的可拓知识表示模型,该模型包括激励基元、测量基元和结论基元3个部分;然后提出了先匹配激励基元再匹配测量基元的可拓知识匹配步骤,并构建了属性精确值和区间值混合的匹配度计算公式;最后以某型大气数据计算机的测试数据为例,与建立的可拓知识进行匹配,诊断结果与实际情况相符。     

Handling large amounts of chemical data

The main aspects of handling large amounts of complex information, in general, and spectroscopic data, in particular, are described and discussed. In the first part, the basic terms and procedures are defined and illustrated with examples from different spectrometries. The items such as the representation of complex data of different types, measurement — and information — pace, metrics of a multi-dimensional space, different types of transformations, information content of a given representation, the concept of a frame, the holistic and reductionistic aspect of information are explained in more detail.In the second part, the organization of complex data with the intention of forming an expert system is discussed. Emphasis is put on the clustering of data, on the criteria for clustering, and on the ways and means governing the formation of hierarchies of clusters and frames they represent. Furtheron, a model for an expert system (which is able to acquire new information, i. e. which is able to learn, and to use the acquired knowledge for predicting the structural features of unknown compounds) based on the hierarchical organization of a large amount of data is outlined.Finally, the prospects and limitations of expert systems based on the hierarchical clustering of large data collections are discussed.     

三好学生的模糊综合评选模型

通过分析目前三好学生评选中存在的不足之处 ,构建了三好学生综合评选指标体系 ,应用模糊综合评价理论建立了三好学生模糊综合评选模型 ,介绍了如何应用专家调查法来确定各指标的权重 .     

智能的统计软件──统计专家系统

计算统计是数理统计的一个重要分支，而统计专家系统是计算统计中一极热门的话题。本文简介了统计专家系统的概念及发展过程，并根据本人所作的工作介绍了统计专家系统的基本结构和构造过程等。最后对未来的统计专家系统提出了一点设想。     

Data analysis and decision support

In this paper, we consider developmental lines of computer-assisted decision support (with consideration of knowledge-based approaches) for data analysis problems. First, we discuss some situations where it is obviously appropriate to apply computer-assisted decision support in connection with data analysis tasks. Then, a brief historical retrospect is given viewing the development of this area of research and its interfaces to knowledge-based approaches. Against this background we illustrate two prototypes of knowledge-based decision support systems for specific data-analysis problems related to fields of interest of our own. Finally, we indicate possible progress and future activities in this area.     

Chemometrics for material analysis

Chemometric methods are described that provide effective means to enhance the information of analytical methods in material analysis and to enable knowledge engineering tools to be applied within the field. Typical applications are outlined for depth-profile analysis, for pattern recognition on ceramic systems as well as for the utilization of expert systems.     

计算机辅助有机化合物结构解析ESESOC-Ⅱ

简要介绍了有机化合物结构自动解析专家系统ＥＳＥＳＯＣ－Ⅱ及其结构产生器由分子式穷举生成有机化合物结构异构体的基本算法。主要讨论了结构产生器的穷举性、非冗性和有效性问题。     

An Expert System for EPMA

 Experimental parameters for an electron microprobe are numerous and their choice influence the quality and accuracy of the analysis results. The expert system intends to optimise the choice of every parameter as well as to automate the stages of a quantitative analysis for all kinds of materials. In short, the expert system aims to master the accuracy of results and to control the time of analysis. The starting point of the expert system is an interactive questionnaire about the sample and about the expectations of the analysis (the accuracy of the result and/or the duration of the analysis). Then, the expert system makes a semi-quantitative analysis on the sample. It gives the sample composition in a first approximation. This is necessary for the expert system to optimise all the parameters for an accurate quantitative analysis. Each parameter is modelled by a specific algorithm. The expert system selects the parameters by minimising, at every stage, the statistical error generated by the algorithm. In this way, it operates in a sufficiently independent way to create a quantitative analysis configuration adapted to an unknown sample. The expert system employs the usual methods of quantification but the results are obtained in half the time. Moreover, the accuracy of the result is guaranted by the statistical error calculation as the expert system is running.