集对分析在不确定性智能决策中的应用

语言是思维的表达，智能决策是基于确定性与不确定性对立统一思维的一类高级决策。文章综述集对分析在纯自然语言决策，自然语言与数学混合语言决策，区间数决策和直觉模糊决策，集对分析粗糙集决策，联系数与马尔可夫链相结合的决策，赵森烽?克勤概率的贝叶斯决策，偏联系数的决策和同异反综合集成决策等方面的应用。特点是把基于确定性的决策建模与不确定性系统分析相结合，把系统宏观层次的分析与微观层次的分析相结合，把两种或多种决策方法综合集成，根据不确定性的具体情况给出决策建议，因而是一种立足于全局的智能决策，并认为集对分析的不确定性智能决策过程，在本质上是把决策系统中的信息能转换成智能的过程。     

概率粗糙集模型在机械故障诊断中的应用

机械故障产生的机理比较多且表现形式具有不确定性,概率粗糙集模型弥补了Pawlak粗糙集模型在解决知识不确定性决策问题时的不足。概率粗糙集模型能充分利用近似边界区域提供的统计信息,并能对给定概念一个更完整的刻画,因而可以提取带有确定因子的决策规则。首先论述了概率粗糙集模型并引进了概率粗糙集模型的属性约简,然后介绍了在机械故障诊断中有关Bayes决策问题的概率粗糙集模型,最后用一个实例说明概率粗糙集模型在机械故障诊断中的应用。     

基于模糊粗糙集和D-S证据理论的多源灌溉信息融合方法

针对多源灌溉信息决策过程中不确定性信息难以融合的问题,提出了一种基于模糊粗糙集和D-S证据理论相结合的决策融合方法。运用模糊粗糙集理论,建立基本概率分配函数,计算各灌溉因子与灌溉决策之间的依赖程度,构建多个融合灌溉因子对灌溉决策的识别框架;然后运用改进的D-S证据理论,进行多源灌溉信息决策层级的融合,最终解决不确定信息的表达和合成问题。应用上述方法对华北地区冬小麦土壤水分、光合速率和气孔导度等信息进行灌溉决策融合,结果显示：灌溉决策的不确定性由融合前的最高38%降至9.84%,该方法可有效地提高灌溉决策精度,降低灌溉决策的不确定性     

Intuitionistic fuzzy rough set model based on conflict distance and applications

Due to the complexity and uncertainty of the objective world, as well as the limitation of human ability to understand, it is difficult for one to employ only a single type of uncertainty method to deal with the real-life problem of decision-making, especially problems involving conflicts. On the other hand, by incorporating the advantages of various theories of uncertainty, one is expected to develop a more powerful hybrid method for soft decision making and to solve such problems more effectively. In view of this, in this paper the thought and method of intuitionistic fuzzy set and rough set are used to construct a novel intuitionistic fuzzy rough set model. Corresponding to the fact that the decision-making information system of rough sets is of intuitionistic fuzzy information system, our method defines the conflict distance by using the idea of measuring intuitionistic fuzzy similarity so that it is introduced into the models of rough sets, leading to the development of our intuitionistic fuzzy rough set model. After that, we investigate the properties of the model, introduce a novel tool for conflict analysis based on our hybrid model, and employ this new tool to describe and resolve a real-life conflict problem.     

不确定性信息表示及推理

在基于数据的复杂系统建模过程中,各种不确定性信息普遍存在.一般而言,客观系统的随机性与人类认知的模糊性构成了不确定性的最基本内涵.为了对不确定性信息进行形式化的描述,促进人类对实际系统的理解,近年来各种不确定性理论得到极大发展.基于此,首先给出不确定性的来源、分类及特点;然后,从随机性、模糊性及混合不确定性3方面系统梳理贝叶斯推理、模糊推理、粗糙集、灰色理论和证据理论等方法在不确定性信息表示与推理方面的研究,同时总结分析上述理论在可靠性工程、信息融合和决策支持等方面的典型应用;最后,在对现有工作简要总结的基础上,提出不确定性理论在未来发展中面临的三大挑战,并给出潜在的解决思路,以期为该领域的研究者提供一定的参考.     

Developing new processes for COTS-based systems

Although commercial off-the-shelf (COTS) products are becoming increasingly popular, little information is available on how they affect existing software development processes or what new processes are needed. At Carnegie Mellon University's Software Engineering Institute (SEI), we are developing a process framework for working with COTS-based systems     

Towards a Software Change Classification System: A Rough Set Approach

The basic contribution of this paper is the presentation of two methods that can be used to design a practical software change classification system based on data mining methods from rough set theory. These methods incorporate recent advances in rough set theory related to coping with the uncertainty in making change decisions either during software development or during post-deployment of a software system. Two well-known software engineering data sets have been used as means of benchmarking the proposed classification methods, and also to facilitate comparison with other published studies on the same data sets. Two technologies in computation intelligence (CI) are used in the design of the software change classification systems described in this paper, namely, rough sets (a granular computing technology) and genetic algorithms. Using 10-fold cross validated paired t-test, this paper also compares the rough set classification learning method with the Waikato Environment for Knowledge Analysis (WEKA) classification learning method. The contribution of this paper is the presentation of two models for software change classification based on two CI technologies.     

An integrated fuzzy multi criteria group decision making approach for ERP system selection

This paper aims to ease group decision-making by using an integration of fuzzy AHP (analytic hierarchy process) and fuzzy TOPSIS (technique for order preference by similarity to ideal solution) and its application to software selection of an electronic firm. Firstly, priority values of criteria in software selection problem have been determined by using fuzzy extension of AHP method. Fuzzy extension of AHP is suggested in this paper because of little computation time and much simpler than other fuzzy AHP procedures. Then, the result of the fuzzy TOPSIS model can be employed to define the most appropriate alternative with regard to this firm's goals in uncertain environment. Fuzzy numbers are presented in all phases in order to overcome any vagueness in decision making process. The final decision depends on the degree of importance of each decision maker so that wrong degree of importance causes the mistaken result. The researchers generally determine the degrees of importance of each decision maker according to special characteristics of each decision maker as subjectivity. In order to overcome this subjectivity in this paper, the judgments of decision makers are degraded to unique decision by using an attribute based aggregation technique. There is no study about software selection using integrated fuzzy AHP-fuzzy TOPSIS approach with group decision-making based on an attribute based aggregation technique. The results of the proposed approach and the other approaches are compared. Results indicate that our methodology allows decreasing the uncertainty and the information loss in group decision making and thus, ensures a robust solution to the firm.     

基于D-U空间的混合型多属性决策方法

针对混合型决策中不同类型属性值无法有效保留其不确定信息的问题,根据不确定原理,将联系数的D-U空间理论引入到混合型多属性决策问题中,提出一种基于D-U空间的混合型多属性决策方法。将确定性与不确定视为一个整体,提出不同类型属性值在D-U空间中的映射转换法则,使得不同类型属性值在空间中得以统一量化,并明确属性中的不确定信息,避免不确定信息丢失而造成的决策偏差。在决策过程中,通过计算空间中属性向量的模和幅角的值进行方案选择,以描述各方案的稳定性,使排序准则具有直观的意义。通过2个算例验证了该方法的适用性和实用性。     

A survey of approaches for the visual model-driven development of next generation software-intensive systems

Software-intensive systems of the future are expected to be highly distributed and to exhibit adaptive and anticipatory behavior when operating in highly dynamic environments and interfacing with the physical world. Therefore, visual modeling techniques to address these software-intensive systems require a mix of models from a multitude of disciplines such as software engineering, control engineering, and business process engineering. As in this concert of techniques software provides the most flexible element, the integration of these different views can be expected to happen in the software. The software thus includes complex information processing capabilities as well as hard real-time coordination between distributed technical systems and computers.In this article, we identify a number of general requirements for the visual model-driven specification of next generation software-intensive systems. As business process engineering and software engineering are well integrated areas and in order to keep this survey focused, we restrict our attention here to approaches for the visual model-driven development of adaptable software-intensive systems where the integration of software engineering with control engineering concepts and safety issues are important. In this survey, we identify requirements and use them to classify and characterize a number of approaches that can be employed for the development of the considered class of software-intensive systems.     

A chance-constrained portfolio selection model with random-rough variables

Traditional portfolio selection (PS) models are based on the restrictive assumption that the investors have precise information necessary for decision-making. However, the information available in the financial markets is often uncertain. This uncertainty is primarily the result of unquantifiable, incomplete, imprecise, or vague information. The uncertainty associated with the returns in PS problems can be addressed using random-rough (Ra-Ro) variables. We propose a new PS model where the returns are stochastic variables with rough information. More precisely, we formulate a Ra-Ro mathematical programming model where the returns are represented by Ra-Ro variables and the expected future total return maximized against a given fractile probability level. The resulting change-constrained (CC) formulation of the PS optimization problem is a non-linear programming problem. The proposed solution method transforms the CC model in an equivalent deterministic quadratic programming problem using interval parameters based on optimistic and pessimistic trust levels. As an application of the proposed method and to show its flexibility, we consider a probability maximizing version of the PS problem where the goal is to maximize the probability that the total return is higher than a given reference value. Finally, a numerical example is provided to further elucidate how the solution method works.

     

基于模型法应对企业信息系统工程中不确定性需求

在软件工程或具体的需求工程中,用户需求通常具有不确定性.这成为了企业信息化实践中的主要问题之一;在企业信息系统工程中这尤其是一关键问题.通过扩展模型的概念、分析企业领域中可用模型的情况,提出了一种基于模型来应对用户需求之不确定性的方法.描述了应用基于模型的方法确定企业信息系统需求的基本逻辑与主要活动过程,并给出了一个应用ARIS(集成信息系统结构)参考模型库解决需求问题的实例.研究表明,基于模型的方法可用于有效地应对企业信息系统工程中的不确定性需求.     

Fuzzy rough DEA model: A possibility and expected value approaches

Uncertainty is certain in the world of uncertainty. Measuring the performance of any entity in such an uncertain environment is unavoidable. Fuzzy rough data envelopment analysis (FRDEA) provides a room to evaluate the relative efficiency of homogenous entities, widely known as decision making units (DMUs) in the data envelopment analysis (DEA) literature. This paper attempts to create a fuzzy rough DEA model by integrating the classical DEA, fuzzy set theory, and rough set theory, which apparently provide a way to accommodate the uncertainty. Moreover, in contrast to the probability approach, this paper provides a pavement to measure the relative efficiency of any given DMUs in line with the possibility approach along with the fuzzy rough expected value operator.     

覆盖粗糙直觉Fuzzy集模型

考虑到经典粗糙集模型中等价关系过于严格的缺陷和直觉Fuzzy集在处理不确定信息时所具有的表达力,建立了覆盖粗糙直觉Fuzzy集模型,并给出了该模型下的一些性质;接着引入了覆盖粗糙直觉Fuzzy集模型的粗糙度和粗糙熵的概念,讨论其不确定性度量;最后给出了算例。     

From pixels to multi-robot decision-making: A study in uncertainty

Mobile robots must cope with uncertainty from many sources along the path from interpreting raw sensor inputs to behavior selection to execution of the resulting primitive actions. This article identifies several such sources and introduces methods for (i) reducing uncertainty and (ii) making decisions in the face of uncertainty. We present a complete vision-based robotic system that includes several algorithms for learning models that are useful and necessary for planning, and then place particular emphasis on the planning and decision-making capabilities of the robot. Specifically, we present models for autonomous color calibration, autonomous sensor and actuator modeling, and an adaptation of particle filtering for improved localization on legged robots. These contributions enable effective planning under uncertainty for robots engaged in goal-oriented behavior within a dynamic, collaborative and adversarial environment. Each of our algorithms is fully implemented and tested on a commercial off-the-shelf vision-based quadruped robot.     

一种不确定性条件下的自适应知识学习方法

通过研究决策表和决策规则的不确定性，分析了由不分明关系划分的粒度引起的规则不确定性的两个方面，即不一致性和随机性，建立基于信息熵和粗糙集表示的不确定性信息度量的方法．利用该方法计算决策表局部最小确定性，并以此为阈值来控制规则集生成的数量，避免不必要的冗余规则的生成．同时结合Skowron的缺省规则获取算法，实现了没有领域先验知识条件下的不确定知识的自适应学习过程．试验结果表明．阈值的选取是合理的，在保持较高的决策正确率的同时，有效地控制了规则集的生成．     

基于粗糙集理论的遥感影像分类研究

粗糙集理论作为一种新的处理含糊和不确定性问题的数学工具,可以有效地分析和处理不完备信息,已经在模式识别、机器学习、决策支持、过程控制、预测建模等众多科学与工程领域得到成功的应用,并具有相当的发展潜力,该文在深入研究粗集理论基础上,将其引入遥感影像的处理中,对遥感图像分类进行了系统的研究。文中基于图像的粗糙集知识系统,提出了一种新的遥感图像知识分类算法———粗糙分类法,最后给出了一个相应的实例。     

Engineering computation under uncertainty - Capabilities of non-traditional models

This paper provides a review of various non-traditional uncertainty models for engineering computation and responds to the criticism of those models. This criticism imputes inappropriateness in representing uncertain quantities and an absence of numerically efficient algorithms to solve industry-sized problems. Non-traditional uncertainty models, however, run counter to this criticism by enabling the solution of problems that defy an appropriate treatment with traditional probabilistic computations due to non-frequentative characteristics, a lack of available information, or subjective influences. The usefulness of such models becomes evident in many cases within engineering practice. Examples include: numerical investigations in the early design stage, the consideration of exceptional environmental conditions and socio-economic changes, and the prediction of the behavior of novel materials based on limited test data. Non-traditional uncertainty models thus represent a beneficial supplement to the traditional probabilistic model and a sound basis for decision-making. In this paper non-probabilistic uncertainty modeling is discussed by means of interval modeling and fuzzy methods. Mixed, probabilistic/non-probabilistic uncertainty modeling is dealt with in the framework of imprecise probabilities possessing the selected components of evidence theory, interval probabilities, and fuzzy randomness. The capabilities of the approaches selected are addressed in view of realistic modeling and processing of uncertain quantities in engineering. Associated numerical methods for the processing of uncertainty through structural computations are elucidated and considered from a numerical efficiency perspective. The benefit of these particular developments is emphasized in conjunction with the meaning of the uncertain results and in view of engineering applications.     

Decider: A fuzzy multi-criteria group decision support system

Multi-criteria group decision making (MCGDM) aims to support preference-based decision over the available alternatives that are characterized by multiple criteria in a group. To increase the level of overall satisfaction for the final decision across the group and deal with uncertainty in decision process, a fuzzy MCGDM process (FMP) model is established in this study. This FMP model can also aggregate both subjective and objective information under multi-level hierarchies of criteria and evaluators. Based on the FMP model, a fuzzy MCGDM decision support system (called Decider) is developed, which can handle information expressed in linguistic terms, boolean values, as well as numeric values to assess and rank a set of alternatives within a group of decision makers. Real applications indicate that the presented FMP model and the Decider  software are able to effectively handle fuzziness in both subjective and objective information and support group decision-making under multi-level criteria with a higher level of satisfaction by decision makers.     

云平台下基于粗糙集的并行增量知识更新算法

日益复杂和动态变化的海量数据处理,是当前人们普遍关注的问题,其核心内容之一是研究如何利用已有的信息实现快速的知识更新.粒计算是近年来新兴的一个研究领域,是信息处理的一种新的概念和计算范式,主要用于描述和处理不确定的、模糊的、不完整的和海量的信息,以及提供一种基于粒与粒间关系的问题求解方法.作为粒计算理论中的一个重要组成部分,粗糙集是一种处理不确定性和不精确性问题的有效数学工具.根据云计算中的并行模型MapReduce,给出了并行计算粗糙集中等价类、决策类和两者之间相关性的算法;然后,设计了用于处理大规模数据的并行粗糙近似集求解算法.为应对动态变化的海量数据,结合MapReduce模型和增量更新方法,根据不同的增量策略,设计了两种并行增量更新粗糙近似集的算法.实验结果表明,该算法可以有效地快速更新知识;而且数据量越大,效果越明显.