An illustration of verification and validation in the modelling phase of KBS development

Reliability has become a key factor in KBS development. For this reason, it has been suggested that verification and validation (V&V) should become an integrated part of activities throughout the whole KBS development cycle. In this paper, it will be illustrated how the PROLOGA workbench integrates V&V aspects into its modelling environment, such that these techniques can be of assistance in the process of knowledge acquisition and representation. To this end, verification has to be performed incrementally and can no longer be delayed until after the system has been completed. It will be shown how this objective can be realised through an approach that uses the decision table formalism as a modelling instrument.     

数据挖掘在现代作战中的应用研究

随着装备技术的发展,现代作战具有作战单元种类数量多、攻防战术复杂、干扰性欺骗性数据充斥等特点,尤其是协同作战能力和数据交换能力逐步增强,以上都使得现代作战中涉及的信息呈现＂海量＂特征。为了取得理想的作战效果,指挥员必须对以上海量信息进行处理,从中提取出有用的信息和知识。近年来迅速发展的数据挖掘技术,在处理海量信息方面具有非常明显的优势。文章在介绍现代作战特点和数据挖掘技术的基础上,对数据挖掘技术在现代作战中的应用做了初步的研究。     

MULTICRITERIA SORTING METHODOLOGY: APPLICATION TO FINANCIAL DECISION PROBLEMS

Abstract

The primary objective in the sorting approach is to assign a set of alternatives into predefined classes. This type of problem is often encountered in many real world decision problems. During the last two decades several new approaches have been proposed to overcome the shortcomings of traditional statistical and econometric techniques. This paper focuses on the multicriteria decision aid (MCDA) approach; it briefly reviews the main MCDA sorting techniques, and presents the multigroup hierarchical discrimination method. This new MCDA sorting technique is applied to the portfolio selection problem. A comparison with discriminant analysis is also performed. Furthermore, the efficiency of the proposal approach can be easily improved for solving large-scale problems in a multiprocessing environment.     

基于决策树的数据挖掘算法优化研究

决策树模型是数据挖掘中最常用的一种方法,具有较好的分类预测能力,并能方便提取决策规则。基于相似性原理,以测试属性和决策属性的相似度作为启发规则构建决策树。提出了一种新的决策树生成算法。并在高校教师综合考评系统中采用了这种新算法,实验结果表明这种新的决策树生成算法预测精度较高,计算也比较简便。     

基于Web的决策支持系统的设计与实现

实现了一个基于Web的原型决策支持系统。该系统依据以往天气条件的记录,递归地构建一个决策树,用其预测未来网球比赛能否举行。详细展示了一个原型决策支持系统开发周期,包括从分析、设计到实现,如何使用面向对象技术来开发系统。     

Transformation of semantic knowledge into simulation-based decision support

Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, in-depth system expertise is inevitable for mapping objects and constraints from the real world to a virtual model. This knowledge-intensity leads to long development times of respective projects, which contradicts the need for timely decision support. Since more and more companies use industrial knowledge graphs and ontologies to foster their knowledge management, this paper proposes a framework on how to efficiently derive a simulation model from such semantic knowledge bases. As part of the approach, a novel Simulation Ontology provides a standardized meta-model for hybrid simulations. Its instantiation enables the user to come up with a fully parameterized formal simulation model. Newly developed Mapping Rules facilitate this process by providing guidance on how to turn knowledge from existing ontologies, which describe the system to be simulated, into instances of the Simulation Ontology. The framework is completed by a parsing procedure for an automated transformation of this conceptual model into an executable one. This novel modeling approach makes model development more efficient by reducing its complexity. It is validated in a use case implementation from semiconductor manufacturing, where cross-domain knowledge was required in order to model and simulate the impacts of the COVID-19 pandemic on a global supply chain network.     

Entropy/cross entropy-based group decision making under intuitionistic fuzzy environment

We study the group decision making problem under intuitionistic fuzzy environment. Based on entropy and cross entropy, we give two methods to determine the optimal weights of attributes, and develop two pairs of entropy and cross entropy measures for intuitionistic fuzzy values. Then, we discuss the properties of these measures and the relations between them and the existing ones. Furthermore, we introduce three new aggregation operators, which treat the membership and non-membership information fairly, to aggregate intuitionistic fuzzy information. Finally, several practical examples are presented to illustrate the developed methods.     

Interactive Genetic Algorithm with Mixed Initiative Interaction for multi-criteria ground water monitoring design

Design of optimal plans for environmental planning and management applications should ideally consider the multiple quantitative and qualitative criteria relevant to the problem. For example, in ground water monitoring design problems, qualitative criteria such as acceptable spatial extent and shape of the contaminant plume predicted from the monitored locations can be equally important as the typical quantitative criteria such as economic costs and contaminant prediction accuracy. Incorporation of qualitative criteria in the problem-solving process is typically done in one of two ways: (a) quantifying approximate representations of the qualitative criteria, which are then used as additional criteria during the optimization process, or (b) post-optimization analysis of designs by experts to evaluate the overall performance of the optimized designs with respect to the qualitative criteria. These approaches, however, may not adequately represent all of the relevant qualitative information that affect a human expert involved in design (e.g. engineers, stakeholders, regulators, etc.), and do not necessarily incorporate the effect of the expert's own learning process on the suitability of the final design. The Interactive Genetic Algorithm with Mixed Initiative Interaction (IGAMII) is a novel approach that addresses these limitations by using a collaborative human-computer search strategy to assist users in designing optimized solutions to their applications, while also learning about their problem. The algorithm adaptively learns from the expert's feedback, and explores multiple designs that meet her/his criteria using both the human expert and a simulated model of the expert's responses in a collaborative fashion. The algorithm provides an introspection-based learning framework for the human expert and uses the human's subjective confidence measures to adjust the optimization search process to the transient learning process of the user. This paper presents the design and testing of this computational framework, and the benefits of using this approach for solving groundwater monitoring design problems.     

Soft computing approach to fault diagnosis of centrifugal pump

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study.