基于错误逻辑相似变换构建网购用户评价有效集模型

基于错误逻辑相似转化联结词,给出了错误逻辑命题的论域、事物、空间、特征、量值、错误值、规则、错误函数、时间等参数的相似变换矩阵定义.文中给出了形式上为T(C_1)=C_2的相似变换错误矩阵方程模型.针对电子商务网购用户评价的网上抓取数据,定义了从包含若干无效评价的大集合向有效小集合变换的错误矩阵模型.模型是基于错误逻辑理论,从已知转化系数矩阵T,以及初始错误矩阵,向未知目标集合进行相似变换的知识推理探索.     

基于模糊错误逻辑的投资结构优化研究

主要研究模糊错误逻辑的运算,设定了模糊错误逻辑的基本运算规则,对模糊错误逻辑公式、变量、真值、函数、字、子句、字组等概念进行定义,在此基础上,提出2个定理并予以证明,揭示了模糊错误逻辑在错误的传递、转化与消除过程中的运算规律和性质 ,然后建立了优化投资结构模糊错误逻辑模型.     

基于错误逻辑矩阵的错误识别对象分类及应用研究

研究了错误识别对象的概念和特征,并选取七个特征组合建立了错误识别对象的错误逻辑矩阵表达式,讨论了错误识别对象的类型.最后,结合石塑地板产品质检过程,对于所进行检验的每一片石塑地板建立错误识别的对象逻辑矩阵,通过错误函数求错误值,识别错误的石塑地板对象,并根据错误石塑地板对象特征分类,采用相应的消避错方法进行运算和处理.     

二类1错误矩阵方程增优置换变换求解及其在城市交通管理中的应用研究

消错学的错误矩阵可表达错误逻辑里所定义的分解、相似、增加、置换、毁灭、单位变换等转化词,针对其中的置换变换,构建了二类1错误矩阵方程增优置换变换错误矩阵方程,并讨论了该类错误矩阵方程的求解.用交通管理问题对错误矩阵进行了举例,并构建相应的错误矩阵方程,利用上述的求解方法,对二类1方程置换变换进行了求解.     

错误系统结构的分解变换研究

对于错误系统的结构分解,需要先弄清楚错误系统的结构,再把该错误系统以某种基本结构分解成若干子系统,然后对子系统又以某种基本结构分解成下一层次的若干子系统,如此下去,直到所有子系统都为基本结构子系统为止.在此基础上,研究以串联、并联、扩缩、蕴含、反馈和其它型等六种基本型为基础的分解变换的分解方式,分解类型.同时采用图说明相应变换,采用系统结构与错误函数定量描述变换的逻辑规律,利用结构与功能的关系定量描述系统结构与错误的关系,最后给出了变换体系.     

错误评价及其消错优化研究

现实中决策者往往希望将评价对象的特征值与期望值之间的偏差控制在可接受范围之内,而且偏差越小越好.基于消错学对错误的界定,利用错误函数对评价对象的特征指标值与评价标准之间的偏差进行了度量,并遵循"区分主次、明确底线、综合集成"的思路,通过建立消错规划模型实施消错优化,从而在保证各特征指标不突破临界错误值的情况下,做到评价对象的整体错误值最小化.最后通过算例分析,演示了消错优化的过程,验证了消错优化模型的合理性和可操作性.     

模糊错误逻辑事物毁灭转化联结词研究

给出模糊错误逻辑事物毁灭转化联结词所涉及的概念、运算及其模糊错误逻辑事物毁灭转化联结词与外延联结词否定,∧合取,∨析取,∨bxr不相容析取,→实质蕴涵等和对模糊错误逻辑事物毁灭转化联结词与内涵联结词∧n内涵合取词,∨n内涵析取,—n内涵差取,/nf l内涵分离,/nf h内涵分化,∥nhb内涵互补,∥-nhd l内涵对立等的关系作了一点研究。     

模糊错误逻辑分解转化词与内涵否定词的逻辑关系

在文 [1]的基础上 ,在简单分析现时中错误的传递与转化的实际存在的同时 ,介绍国内外在逻辑研究领域对事物传递与转化规律的研究现状。特别为了探索控制与防范证券风险的技术而主要研究模糊错误逻辑分解转化词与内涵否定词的逻辑关系。     

同一水平下模糊规则信息熵的描述

给出了模糊知识系统及模糊决策逻辑公式的定义,在此基础上描述了模糊决策逻辑公式及模糊知识系统下模糊规则的信息熵,讨论了模糊规则信息熵的相关性质;其次,利用模糊规则信息熵对模糊规则进行了分类、评价,从而为建立合理的模糊系统提供了一种有效的判定方法;最后,通过实例验证了所提出理论的正确性.     

错误系统结构的相似变换研究

分析了现实世界中事物的可变性,以及人们利用事物可变性的可能性.在此基础上,我们研究了对串联、并联、扩缩、蕴含、反馈等六种基本结构的相似变换的系统结构的图示表示,并利用错误函数和逻辑命题去形式化描述它们的变化形式和规律.接着给出了事物可变性的结构图示和六种基本变换的变换方式,变换参数和变换体系.     

Axiomatic characterization of a general utility function and its particular cases in terms of conjoint measurement and rough-set decision rules

Utility or value functions play an important role of preference models in multiple-criteria decision making. We investigate the relationships between these models and the decision-rule preference model obtained from the Dominance-based Rough Set Approach. The relationships are established by means of special “cancellation properties” used in conjoint measurement as axioms for representation of aggregation procedures. We are considering a general utility function and three of its important special cases: associative operator, Sugeno integral and ordered weighted maximum. For each of these aggregation functions we give a representation theorem establishing equivalence between a very weak cancellation property, the specific utility function and a set of rough-set decision rules. Each result is illustrated by a simple example of multiple-criteria decision making. The results show that the decision rule model we propose has clear advantages over a general utility function and its particular cases.     

Domain structuring for knowledge-based multiattribute classification (a verbal decision analysis approach)

Classification problems in decision making are, at least, ill-structured or even unstructured ones, since, among other things, human judgments (i.e., Decision Maker preferences and/or expert knowledge) are the primary sources of information for their solving. Thus, not only the classification rules eliciting, but the application domain structuring as well, is a complex problem itself. The paper focuses on knowledge-based classification problem structuring in the context of complete (up to the expert knowledge) and consistent knowledge base construction for a Diagnostic Decision Support System. Two structuring techniques are proposed as expert aids, as well as an approach to large-size problem decomposition. It is asserted that application domain structuring and classification rules eliciting have to be arranged as interconnected procedures.     

An approach for predicting and describing patient outcome using multicriteria decision analysis and decision rules

Patient outcome in brain trauma patients is affected by a multiplicity of factors, beginning with ambulatory transportation and routing, to the grade of the receiving facility and treatment therein, and finally the treatment and monitoring in definitive care (the brain trauma intensive care unit). Factors and events in each of these phases can be modeled as a multicriteria problem, where the objective is to optimize patient outcome; moreover, a more comprehensive model can embody the interactions of all three phases. This study focuses on modeling the factors that affect patient outcome in definitive care and on expressing these in machine readable format so that we can better describe or predict patient outcome using data mining tools. We use multicriteria decision analysis and decision rules for knowledge representation. Preliminary results suggest that the incorporation of a priori knowledge does help better predict or describe patient outcome when using decision tree induction.     

On the risk-equivalence of two methods of randomization in statistics

For statistical decision problems, there are two well-known methods of randomization: on the one hand, randomization by means of mixtures of nonrandomized decision functions (randomized decision rules) in the game “statistician against nature,” on the other hand, randomization by means of randomized decision functions. In this paper, we consider the problem of risk-equivalence of these two procedures, i.e., imposing fairly general conditions on a nonsequential decision problem, it is shown that to each randomized decision rule, there is a randomized decision function with uniformly the same risk, and vice versa. The crucial argument is based on rewriting risk-equivalence in terms of Choquet's integral representation theorem. It is shown, in addition, that for certain special cases that do not fulfill the assumptions of the Main Theorem, risk-equivalence holds at least partially.     

Multiperson decision-making based on multiplicative preference relations

A multiperson decision-making problem, where the information about the alternatives provided by the experts can be presented by means of different preference representation structures (preference orderings, utility functions and multiplicative preference relations) is studied. Assuming the multiplicative preference relation as the uniform element of the preference representation, a multiplicative decision model based on fuzzy majority is presented to choose the best alternatives. In this decision model, several transformation functions are obtained to relate preference orderings and utility functions with multiplicative preference relations. The decision model uses the ordered weighted geometric operator to aggregate information and two choice degrees to rank the alternatives, quantifier guided dominance degree and quantifier guided non-dominance degree. The consistency of the model is analysed to prove that it acts coherently.     

A fifth generation numerical modelling system in coastal zone

Nowadays, artificial intelligence (AI) technology is gradually integrated into the numerical modelling system to make the system more intelligent and more user-friendly. The characteristics of the fifth generation numerical modelling are connected with AI applications. The expert system technology as a widely applied AI technology is integrated into our modelling system for coastal water processes with traditional numerical computational tools and the data and graphical pre-processing and post-processing techniques. Five kinds of knowledge bases are built in the system to describe the existing expertise knowledge about model parameters, relations between parameters and physical conditions, various possible selections for parameters and rules of inference. The inference engine is designed to be driven by the confidence of correctness, and the rule base is built with the factor of confidence to link the various relations. The decision tree is designed to drive the inference engine to explore the route of selection procedure of modeling. The decision tree depends on the real problem specifications and can be modified during the dialogue between the system and the user. The forward chaining and backward chaining inference techniques are mixed together in the system to help matching the parameters in the model and the possible selections with sufficiently high confidence. The expert system technology is successfully integrated into the system to provide help for model parameter selection or model selection, and to make the numerical model system more accessible for non-expert users.     

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.     

Selecting informative rules with parallel genetic algorithm in classification problem

The classification system is very important for making decision and it has been attracted much attention of many researchers. Usually, the traditional classifiers are either domain specific or produce unsatisfactory results over classification problems with larger size and imbalanced data. Hence, genetic algorithms (GA) are recently being combined with traditional classifiers to find useful knowledge for making decision. Although, the main concerns of such GA-based system are the coverage of less search space and increase of computational cost with the growth of population. In this paper, a rule-based knowledge discovery model, combining C4.5 (a Decision Tree based rule inductive algorithm) and a new parallel genetic algorithm based on the idea of massive parallelism, is introduced. The prime goal of the model is to produce a compact set of informative rules from any kind of classification problem. More specifically, the proposed model receives a base method C4.5 to generate rules which are then refined by our proposed parallel GA. The strength of the developed system has been compared with pure C4.5 as well as the hybrid system (C4.5 + sequential genetic algorithm) on six real world benchmark data sets collected from UCI (University of California at Irvine) machine learning repository. Experiments on data sets validate the effectiveness of the new model. The presented results especially indicate that the model is powerful for volumetric data set.     

Representation of decision making processes in an ecological evaluation system

This paper deals with the problems of knowledge representation for a decision support system (DSS) applicable in a dynamic environment. Some special principles concerning environment applications are considered in order to understand better human decision making and behavior. The approach of representing static and dynamic aspects of a system and reflecting them using deep knowledge representation is proposed. The formalization of multiple objective decision making mechanisms is considered. The results of modeling cognitive processes leading to decisions are demonstrated by an example developed during the design stages of an ecological evaluation system.