证据的λ—合成

本文讨论了信任测度的直观含意,信任测度的合成及证据组相互独立的问题。回顾了Dempster合成规则,并在揭示其局限性之后给出了一种新的合成规则——λ-合成规则。     

基于相似度证据理论的模糊时间序列模型的研究

为了提高模糊时间序列模型的预测效果,利用证据理论在处理不确定信息和信息融合方面的优越性,利用贴近度作为证据之间的相似度,对模糊规则进行合成,形成基于相似度的证据理论的多因素模糊时间序列模型.方法在支持证据"与"运算的合成和对冲突证据的比例分配上,充分考虑了证据的权重.最后,通过实例的比较研究验证模型的有效性.     

专家系统中证据的合成,传播与修正

在专家系统中，由于证据的不确定性和推理规则的不确定性，推理也相应地发生变化，需要对证据进行合成、传播与修正。有许多文献进行了研究［２］，［３］，［４］，但已有的方法大都是针对不同的不确定性推理给出不同的方法。“本文旨在给出不确定性推理中证据合成、传播与修正的一般公式。     

基于D-S证据理论的群决策专家意见集结方法

将D-S证据理论应用于群决策，指出其优势在于应用基本可信度分配函数描述专家意见，并能区分对所描述对象不知道和否定的差异。在专家意见集结上，Dempster证据合成规则有一定的局限性，当证据发生冲突时，会得到不合理的结果。定义了证据间的分歧度，提出一种专家意见集结方法，使得专家意见发生冲突时亦可得到合理的集结结果。     

证据冲突条件下多传感器目标识别方法比较分析

对于包含雷达、红外、电子支援措施和敌我识别等传感器的综合敌我识别系统,如何对异类传感器输出的不确定性信息进行有效处理是有待解决的基本难题之一.D-S证据理论是一种处理不确定性问题的有效方法.D-S证据理论对高冲突证据合成时会出现融合结果与常理相悖的情况.目前解决该问题主要使用的方法包括改变矛盾信息分配方式的组合规则法和证据信息修正法.从这两方面着手,具体分析在证据冲突情况下不同的目标识别方法,并比较其优劣,为建立综合敌我识别系统提供系统的理论支撑.     

基于Dempster-Shafer证据理论的外汇交易策略研究

自动化交易是现代金融领域的研究热点,而交易策略是其核心。技术指标分析中,每一种指标都有其优势和劣势,单一的指标经常产生导致亏损的虚假信号。为了提高交易信号的质量和可靠性,针对外汇市场的多变性和存在诸多不确定性的客观事实,本文引入证据理论来处理不同技术指标分析方法结论存在的差异;将不同的指标作为独立的证据源,用D-S合成规则对各个指标分析方法的结果予以融合,建立了基于证据理论的多指标融合外汇交易模型,给出了基于证据理论的交易框架。根据技术指标的特点及交易原理,构造了指标证据的基本概率分配函数。最后,通过实例分析验证了该方法的科学性和有效性。     

SLD-博弈树及其删除策略

结合SLD-反驳和对策论的思想,提出标记逻辑程序的SLD-博弈树语义.在SLD-博弈树中,一个目标的所有支持和反对证据作为游戏双方参加博弈.对有限树,提出一种删除策略(博弈规则),根据删除过程的结果判断目标是否成立.对覆盖不循环程序,删除策略是可靠的和完备的.     

基于证据推理与最优指派策略的多属性单边匹配决策

针对匹配中某一方偏好失效的问题,提出一种基于证据推理和最优指派策略的单边匹配方法。一方主体采用多种数据类型描述由对方指定的多个属性信息;另一方给出关于各属性的权重信息;然后,使用证据推理组合规则递推合成多属性及权重信息,以此计算双方的匹配度。在此基础上,运用最优指派策略,建立匹配模型并求解得到匹配结果.实例表明该方法的可行性和有效性。     

两种含可信度的推理

本文旨在建立两种单一结论，多个条件，且含有可信度的推理机制，这种推理机制是非单调的，和不精确的。推理的目的是在计算可信度，从而得到结论成立的可能性大小。这种推理机制有两个特点，特点1是在证据间引入取值于[-1，1]二元函数ξ（A1，A2）反映证据间的相互支持度，有正支持、零支持与负支持之分。在此基础上，建立了可信度计算公式。另一个特点为，当条件是模糊时，引入取值于[0，1]的二元R算子，用来耦合条件模糊隶属度以及规则的可信度，其结果恰好是结论成立的可信度。     

区间信度环境下基于偏好熵的随机格序排列方法

针对偏好优劣关系的信度为区间值的决策偏好系统,运用熵理论提出了一种基于区间值分布偏好向量的决策分析方法。首先,将决策者对方案的偏好描述由:优于、劣于、等价和不可比这四种关系拓广为优于、劣于、等价、无法比较但有上确界、无法比较但有下确界、无法比较且有上确界又下确界、不可比七种偏好关系,并结合区间证据的概念和性质给出了决策偏好系统的区间值分布偏好向量与相对熵的概念、性质。然后,构建了基于偏好熵的证据推理非线性优化模型,通过求解模型,并结合优先原则和集结规则将个人偏好集结成群体偏好,给出了该决策方法的具体步骤,举例说明了方法的可行性。     

A granularity-based framework of deduction, induction, and abduction

In this paper, we propose a granularity-based framework of deduction, induction, and abduction using variable precision rough set models proposed by Ziarko and measure-based semantics for modal logic proposed by Murai et al. The proposed framework is based on α-level fuzzy measure models on the basis of background knowledge, as described in the paper. In the proposed framework, deduction, induction, and abduction are characterized as reasoning processes based on typical situations about the facts and rules used in these processes. Using variable precision rough set models, we consider β-lower approximation of truth sets of nonmodal sentences as typical situations of the given facts and rules, instead of the truth sets of the sentences as correct representations of the facts and rules. Moreover, we represent deduction, induction, and abduction as relationships between typical situations.     

博弈训练及其对均衡的影响

训练和学习是博弈中的一对统一体.博弈学习是通过降低博弈语境的不确定性来提高博弈收益,而博弈训练则是针对博弈学习的一种策略.训练者通过可信的信号传递来影响对手的博弈学习结果,改变受训者的信念,从而提高博弈收益.博弈训练的目标可分为事实隐藏和事实揭示.在使用博弈训练时,应遵循"利已、利他、可信、可辩"的原则,从全局的角度审视整个博弈环境,选择利己利他的训练方法,最终取得较优的训练效果.     

Comparison of selection rules for ordinal optimization

The evaluation of performance of a design for complex discrete event systems through simulation is usually very time consuming. Optimizing the system performance becomes even more computationally infeasible. Ordinal optimization (OO) is a technique introduced to attack this difficulty in system design by looking at “order” in performances among designs instead of “value” and providing a probability guarantee for a good enough solution instead of the best for sure. The selection rule, known as the rule to decide which subset of designs to select as the OO solution, is a key step in applying the OO method. Pairwise elimination and round robin comparison are two selection rule examples. Many other selection rules are also frequently used in the ordinal optimization literature. To compare selection rules, we first identify some general facts about selection rules. Then we use regression functions to quantify the efficiency of a group of selection rules, including some frequently used rules. A procedure to predict good selection rules is proposed and verified by simulation and by examples. Selection rules that work well most of the time are recommended.     

诊断和修理航空继电器故障的优化系统

为了减少目前航空继电器用于发现和修理故障的费用 ,本文建立了基于规则、事实以及其自身经验的一个专家系统——适应性诊断系统 ADS( A daptive Diagnostic System) .该系统利用推理对航空继电器检测和替换顺序进行了优化 ,使节省的费用大约为 50 % .     

Linear programming, the simplex algorithm and simple polytopes

In the first part of the paper we survey some far-reaching applications of the basic facts of linear programming to the combinatorial theory of simple polytopes. In the second part we discuss some recent developments concerning the simplex algorithm. We describe subexponential randomized pivot rules and upper bounds on the diameter of graphs of polytopes. © 1997 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.     

模糊概念格

概念格是研究和处理概念内涵与外延确定性关系的数学方法 ,已成为一种有效的数据分析方法。本文进一步探讨概念内涵与外延的不确定关系的模糊映射 ,给出相应的隶属函数的一些基本数学性质 ,证明全体模糊概念构成一个完全格。     

Interval-type and affine arithmetic-type techniques for handling uncertainty in expert systems

Expert knowledge consists of statements _Sj$S_j$ (facts and rules). The facts and rules are often only true with some probability. For example, if we are interested in oil, we should look at seismic data. If in 90% of the cases, the seismic data were indeed helpful in locating oil, then we can say that if we are interested in oil, then with probability 90% it is helpful to look at the seismic data. In more formal terms, we can say that the implication “if oil then seismic” holds with probability 90%. Another example: a bank A trusts a client B, so if we trust the bank A, we should trust B too; if statistically this trust was justified in 99% of the cases, we can conclude that the corresponding implication holds with probability 99%.     

Rough set theory: a novel approach for extraction of robust decision rules based on incremental attributes

Rough set theory is a new data mining approach to manage vagueness. It is capable to discover important facts hidden in the data. Literature indicate the current rough set based approaches can’t guarantee that classification of a decision table is credible and it is not able to generate robust decision rules when new attributes are incrementally added in. In this study, an incremental attribute oriented rule-extraction algorithm is proposed to solve this deficiency commonly observed in the literature related to decision rule induction. The proposed approach considers incremental attributes based on the alternative rule extraction algorithm (AREA), which was presented for discovering preference-based rules according to the reducts with the maximum of strength index (SI), specifically the case that the desired reducts are not necessarily unique since several reducts could include the same value of SI. Using the AREA, an alternative rule can be defined as the rule which holds identical preference to the original decision rule and may be more attractive to a decision-maker than the original one. Through implementing the proposed approach, it can be effectively operating with new attributes to be added in the database/information systems. It is not required to re-compute the updated data set similar to the first step at the initial stage. The proposed algorithm also excludes these repetitive rules during the solution search stage since most of the rule induction approaches generate the repetitive rules. The proposed approach is capable to efficiently and effectively generate the complete, robust and non-repetitive decision rules. The rules derived from the data set provide an indication of how to effectively study this problem in further investigations.     

贪污量化的内生随机增长模型

从现实来看,贪污不是一种个人行为,是由于制度上的漏洞(如监管不严等)而产生的.本文假设贪污与廉政均衡模型服从通常的决策规则,试从量化的指标入手,分析了增大个体收入风险,加大惩贪震摄力、扩大公共开支的均衡关系及其对渎职、贪污行为的影响力.最后,给出关于抑制贪污依其影响力大小的措施的不同结论.     

Business failure prediction using rough sets

A large number of methods like discriminant analysis, logit analysis, recursive partitioning algorithm, etc., have been used in the past for the prediction of business failure. Although some of these methods lead to models with a satisfactory ability to discriminate between healthy and bankrupt firms, they suffer from some limitations, often due to the unrealistic assumption of statistical hypotheses or due to a confusing language of communication with the decision makers. This is why we have undertaken a research aiming at weakening these limitations. In this paper, the rough set approach is used to provide a set of rules able to discriminate between healthy and failing firms in order to predict business failure. Financial characteristics of a large sample of 80 Greek firms are used to derive a set of rules and to evaluate its prediction ability. The results are very encouraging, compared with those of discriminant and logit analyses, and prove the usefulness of the proposed method for business failure prediction. The rough set approach discovers relevant subsets of financial characteristics and represents in these terms all important relationships between the image of a firm and its risk of failure. The method analyses only facts hidden in the input data and communicates with the decision maker in the natural language of rules derived from his/her experience.