基于模糊粗糙集挖掘方法的证券价格预测研究

为了能更好地对证券市场价格进行合理预测，利用模糊粗糙集以及数据挖掘技术对证券市场中的股票价格在给定时问内进行预测。首先利用模糊集和粗糙集方法将股票价格进行预分类，并按时间属性进行分组，而后通过给出的模糊相似关系下的模糊粗糙集计算每组的真值，通过数据挖掘方法获得候选属性，最终得到相应时间段内的有用规则，根据所得规则预测某一时间段内股票价格的变化趋势。测试结果表踢，该方法能获得较高的准确率，证明其有效性较好。     

可拓数据挖掘在高校教学质量评价中的应用

高校在教学和管理工作中积累了大量的数据,但这些数据没有得到有效利用.将可拓数据挖掘技术引入教学领域,从教学评价数据中提取出隐藏在数据之中的有用信息,为教学管理者提供决策支持.首先通过可拓分析,寻找质量达到要求、可进行有效挖掘的教学评价数据,然后对这些数据进行两方面的挖掘:影响教学质量的关键因素挖掘、教学质量与教师特征之间的关联规则挖掘.     

基于综合模拟法与支持向量机的水资源预警分析

提出并确定了哈尔滨市水资源可持续利用预警指标的警度.利用反馈法确定了预警指标的警限,采用2次检验的方法对其预警有效性进行了检验,设计了预警信号灯系统,并对哈尔滨市水资源可持续利用进行监测预警;运用支持向量机的方法预警指标值进行预测,对哈尔滨市水资源可持续利用进行趋势预警,包括单指标趋势预警和多指标趋势预警,进而获得了趋势预警的结果.     

粗糙集方法在路口交通模糊控制中的应用

交通灯的控制是城市交通系统中一个重要的问题.对交通过程采用模糊控制,模糊规则的获取以及模糊规则的客观性问题一直是众多学者努力研究的问题.粗糙集理论的提出主要是用来处理不完整和不确定信息,可以用来观察、测试数据并进行逻辑推理.本文采用粗糙集的方法对模糊规则的生成进行处理,生成相对客观的模糊规则.并结合路口交通的模糊控制问题,阐述该方法的实用性以及生成的模糊规则的客观性.     

有限链上的格蕴涵代数在不确定性库存模型中的应用

格蕴涵代数是一种处理不确定性信息的应用较为广泛的逻辑代数。本文从实际问题出发,选取常用的语言值,建立了有限链上具体的格蕴涵代数,通过格蕴涵代数上二元运算的定义,给出了语言值间的一些常用运算及相应的运算规则,使得不确定性信息可以直接进行运算;进一步将所构建的格蕴涵代数应用于带有不确定性信息的库存管理问题中,建立了相应的库存控制模型,并通过上述语言值间运算规则和数值计算方法求出了使总库存成本最小的经济订货批量。     

基于Lasso+SVM的制造业上市公司财务风险组合预警模型

根据组合预测思想构建了基于Lasso+SVM的制造业上市公司财务风险组合预警模型,包括串联型组合和信息融合型组合两种,并选取22个财务指标建立了财务预警指标体系,对我国86家制造业上市公司的财务状况进行了预测,还与单一风险预警模型预测效果进行了比较,结果发现:财务风险组合预警模型的预测效果明显高于单一预警模型,用第t-1年的财务数据进行预测的准确率达到了95%以上;串联型组合预警模型的预测效果最优,用第t-1和t-2年的财务数据进行预测的准确率分别达到了100%和90%.     

中国股市周期性研究及其价格监控

对股市周期性和股市价格的监控和预警的研究有利于给予投资者相关的信息.为了探究股票市场的周期性,引入带虚拟变量的ARMA-TGARCH-M模型来研究中国股市的周期性.为了对股票市场进行监控和预警,利用基于ARMA-TGARCH-M模型的残差控制图来实现对股票市场的监控和预警.实证结果发现:中国股市存在着显著的正的周一和周二效应,这主要是由于周一和周二在消化周末所发布的信息导致的.通过残差控制图对超过控制限的点进行分析发现基于ARMA-TGARCH-M模型的控制图能够很好地捕捉到股票市场的不受控状态.     

基于IGA-BP神经网络和专家系统的我国银行流动性风险预警研究

以因子分析主成分方法选取预警指标;以免疫遗传算法对BP神经网络收敛速度较慢、易陷入局部极小等缺陷进行改进,形成IGA-BP神经网络.采用IGA-BP神经网络对银行的流动性风险进行预警,并提出以拥有强大知识库的专家系统对其输出的预警信号进行进一步核准和验证,确保预警信号的有效性,构建我国商业银行流动性风险预警机制.     

集对分析在我国经济增长模糊规则提取中的应用

针对粗糙集和模糊聚类方法提取我国经济增长模糊规则算法复杂的问题,把集对分析用于我国31个省市经济增长模糊规则提取。结果显示,不仅算法简明,而且能同时提取宏观层次上的经济增长规则—固定资产投资对GDP的拉动效果要大于人力资源对GDP的拉动效果,而且还从微观层次上揭示出各省的经济增长规则,为我国十二五经济发展规划的实施提供决策参考。     

基于粗糙集理论的区域经济分析

粗糙集理论在保持知识可靠度不变的前提下,通过知识约简,导出其分类规则或决策规则,是分析不确定系统的一种有力的工具。本文运用粗糙集理论的属性约简以及属性值约简方法,结合我国华东地区的经济数据,对该地区的经济发展特征及其变化进行了探讨,实证结果表明产业构成与成本费用利用率是影响华东地区经济的重要因素,优化产业结构,提高经济效益是该地区经济发展的关键,并给出各省市的经济特征规则。     

Incomplete decision contexts: Approximate concept construction,rule acquisition and knowledge reduction

Incomplete decision contexts are a kind of decision formal contexts in which information about the relationship between some objects and attributes is not available or is lost. Knowledge discovery in incomplete decision contexts is of interest because such databases are frequently encountered in the real world. This paper mainly focuses on the issues of approximate concept construction, rule acquisition and knowledge reduction in incomplete decision contexts. We propose a novel method for building the approximate concept lattice of an incomplete context. Then, we present the notion of an approximate decision rule and an approach for extracting non-redundant approximate decision rules from an incomplete decision context. Furthermore, in order to make the rule acquisition easier and the extracted approximate decision rules more compact, a knowledge reduction framework with a reduction procedure for incomplete decision contexts is formulated by constructing a discernibility matrix and its associated Boolean function. Finally, some numerical experiments are conducted to assess the efficiency of the proposed method.     

基于对象导出三支概念格的决策背景规则获取

规则获取是当前形式概念分析领域的研究热点.首先给出了基于对象导出三支概念格间的细于关系,定义了基于对象导出三支概念格的三支弱协调性,并研究了其与经典概念格下的二支弱协调性之间的关系.然后,研究了基于对象导出三支概念格的规则获取,并与经典概念格的规则获取进行了比较.最后,定义了对象导出三支概念的弱闭标记,研究了基于弱闭标记的三支弱协调决策形式背景的规则获取,剔除了冗余规则,并且得到一些新的更为精简的三支规则.     

Multi-confidence rule acquisition and confidence-preserved attribute reduction in interval-valued decision systems

Rule acquisition is one of the most important objectives in the analysis of decision systems. Because of the interference of errors, a real-world decision system is generally inconsistent, which can lead to the consequence that some rules extracted from the system are not certain but possible rules. In practice, however, the possible rules with high confidence are also useful in making decision. With this consideration, we study how to extract from an interval-valued decision system the compact decision rules whose confidences are not less than a pre-specified threshold. Specifically, by properly defining a binary relation on an interval-valued information system, the concept of interval-valued granular rules is presented for the interval-valued decision system. Then, an index is introduced to measure the confidence of an interval-valued granular rule and an implication relationship is defined between the interval-valued granular rules whose confidences are not less than the threshold. Based on the implication relationship, a confidence-preserved attribute reduction approach is proposed to extract compact decision rules and a combinatorial optimization-based algorithm is developed to compute all the reducts of an interval-valued decision system. Finally, some numerical experiments are conducted to evaluate the performance of the reduction approach and the gain of using the possible rules in making decision.     

Decomposition of a complex fuzzy controller for the truck-and-trailer reverse parking problem

The use of fuzzy logic has, in the last twenty years, become standard practice in the field of control. The reason lies in the fuzzy logic’s ability to relatively quickly transfer uncertain experience and knowledge about the observed object’s behaviour into the process of decision making. Nevertheless, one of the biggest problems that arises when using a fuzzy approach is the large number of fuzzy rules that have to be processed in order to produce one decision (i.e. one control output). The number of rules in a fuzzy controller primarily originates from the number of input variables that are entering the decision process and one possible solution for decreasing it is to use the method of decomposition. Its main goal is to implement the equivalent control functionality with a hierarchy of simpler fuzzy controllers. Their main characteristic is a lower number of input variables, which as a consequence leads to a smaller number of fuzzy rules. In our paper we apply the decomposition approach to the classical complex control case of the Truck-and-Trailer (T&T) reverse parking control problem. In such cases the implementation of control using only one fuzzy controller is very complex and the existing solutions, in some details, even deviate from the classical fuzzy approach. Our solution is, on the other hand, based only on the uncertain knowledge about the behaviour of the T&T driver and the results achieved are even better than those achieved by using the existing solutions.     

基于错误逻辑分解转化联结词的生态文明指标知识表达模型研究

研究错误逻辑的知识表达模型,以错误逻辑理论结合生态文明"五位一体"所构建的生态文明建设指标体系,进行基于对象识别的知识表达.指标体系内的各元素分别被定义为错误逻辑模型中的事物、特征、函数和规则.建模时,首先进行事物分解,第二步进行特定事物下对应的特性及规则分解,最后根据判别规则G对错误函数f形式的影响,对各项指标所适用的错误函数类型进行分类.对象的生成可以为用矩阵这样的数据结构对逻辑知识进行系统化组织做前期准备.     

A knowledge acquisition method for determining utilities of linguistic values for product factors

This paper proposes a fuzzy knowledge acquisition method to discover simplified fuzzy if-then rules, where the antecedent and consequent parts of a fuzzy if-then rule are referred to as a combination of linguistic values and the corresponding utility, respectively, from questionnaire data regarding the consumers’ subjective evaluation for a product or service. The main aim of the proposed method is to support decision makers in making appropriate marketing strategies, by identifying factors of concern to consumers through the analysis of the combinations of linguistic values with higher or lower utilities. To demonstrate the usefulness of the proposed method, computer simulations and possible marketing strategy analysis are performed on the rice taste data and the questionnaire data that evaluates the service quality of fast food stores.     

A survey of fuzzy decision tree classifier

Decision-tree algorithm provides one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Over the years, additional methodologies have been investigated and proposed to deal with continuous or multi-valued data, and with missing or noisy features. Recently, with the growing popularity of fuzzy representation, some researchers have proposed to utilize fuzzy representation in decision trees to deal with similar situations. This paper presents a survey of current methods for Fuzzy Decision Tree (FDT) designment and the various existing issues. After considering potential advantages of FDT classifiers over traditional decision tree classifiers, we discuss the subjects of FDT including attribute selection criteria, inference for decision assignment and stopping criteria. To be best of our knowledge, this is the first overview of fuzzy decision tree classifier.     

一类分布鲁棒线性决策随机优化研究

随机优化广泛应用于经济、管理、工程和国防等领域,分布鲁棒优化作为解决分布信息模糊下的随机优化问题近年来成为学术界的研究热点.本文基于φ-散度不确定集和线性决策方式研究一类分布鲁棒随机优化的建模与计算,构建了易于计算实现的分布鲁棒随机优化的上界和下界问题.数值算例验证了模型分析的有效性.