序信息系统上的优势关系与规则获取

本文利用优势基粗糙集方法主要讨论了序信息系统中对象集的上下近似、决策规则的发现和知识约简等问题。     

基于粗糙集与信息熵的不完备测试信息条件下故障诊断

针对测试信息不完备条件下故障诊断决策问题,引入粗糙集和信息熵方法。利用决策属性支持度求相对核,以此作为启发信息;通过建立属性知识与信息熵的联系,提出基于信息熵的属性约简方法。结合决策属性支持度和信息熵设计了约简算法流程,减少属性集搜索空间,求得到最优属性约简集。实例表明,该算法适用于协调的不完备决策信息系统和不协调的不完备决策信息系统,能够解决不完备测试信息条件下故障诊断决策问题。     

多决策覆盖信息系统的约简

覆盖决策信息系统是研究热点之一,其中对于决策为覆盖的覆盖决策信息系统研究较少.本文利用特征函数将覆盖决策信息系统中以覆盖刻画的决策转化为由0和1组成的形式背景,进而给出了多决策覆盖信息系统的定义.在多决策覆盖信息系统中,通过构造相应的辨识矩阵,讨论了两类覆盖的约简,并研究了这两类约简对应的覆盖协调集与覆盖决策信息系统中...     

一种模糊Rough决策方法

利用模糊集理论和粗糙集理论在处理不确定性和不精确性问题方面侧重点的差异性，构造一种组合决策模型。该模型从问题领域内的部分不精确信息出发利用模糊聚类方法构造一个决策信息系统，利用粗糙集理论关于决策规则的约简方法从决策信息系统中提取（挖掘）决策规则，使之适用于问题的整个领域。     

改模知识归纳方法中的知识相容关系研究

结合改模方案分层递阶表达模型和信息系统同态的定义分析方案结构化处理,论证了方案结构化处理同为信息系统同态。利用信息系统同态的相关定理和推论,分析特征约简与信息系统同态不变的关系,论述了方案结构化处理和特征约简操作保持了改模方案特征决策表之间的相容关系,进而得出不同方案层次级别下的注塑模改模知识之间具有相容关系（即不会存在矛盾）的结论,从而阐明了改模方案分层递阶表达模型及相关粗糙集发现方法在改模知识发现上的合理性。     

基于粗糙集理论的建筑节能综合评价方法

为了提高建筑节能评价的客观性,根据现行建筑节能评价体系中各分项指标对建筑能耗的影响趋势以及建筑节能设计标准中的相关规定和技术要求,结合dest软件计算值,建立建筑节能综合评价决策表,然后采用等距离方法对决策属性值离散、Jelonek算法对属性约简、数据分析法对条件属性值约简,得出并行推理规则,去除重复推理规则得到规则约简。因此,粗糙集理论无需对知识或数据的局部给予主观评价,也就是粗糙集理论对不确定性的程度的描述相对客观。该方法和相关软件相结合,既能处理数据库信息系统,挖掘潜在规律,又克服处理问题精确化;这对于实现建筑节能评价的智能化提供了很好的途径。     

覆盖决策系统的相对约简

一个知识相对于另一个知识的关系在实际应用中十分重要,而覆盖是一种重要的知识.本文讨论知识的相对约简,保持一个覆盖相对于一族覆盖的正域不变的条件下,提出覆盖相对约简的概念,给出覆盖相对协调集和覆盖相对核心的判定定理,推广了不协调覆盖决策系统相对约简理论,该理论对于知识的相对约简有重要意义.     

MMAS与粗糙集在齿轮箱故障诊断中的应用

粗糙集理论是一种新的处理含糊和不确定性知识的数学工具,属性约简是粗集理论研究的重要内容,属性约简算法有很多种,而计算一个最佳约简是NP难问题。为了能够有效地获取信息系统的约简,提出了一种新的约简算法。该算法选择最大-最小蚂蚁系统(MMAS),以Fisher准则作为启发式信息来提高搜索效率,将蚁群优化算法引入属性约简中,利用粗糙集理论对故障诊断决策表进行约简,形成清晰、简明的故障诊断规则,为下一步的故障诊断打下了坚实的基础。     

集值信息系统的属性约简

属性约简是粗糙集理论研究中的重要内容之一。本文主要研究集值信息系统的属性约简问题。在集值信息系统中基于拟序关系引入了信息量的概念,给出了属性特征的判定方法,以及信息量与属性约简之间的关系。根据信息量定义了属性重要性,研究了属性重要性与属性约简之间的关系。进而得到了基于信息量和属性重要性的属性约简算法,给出了该算法的时间复杂度。通过实例说明,该算法是有效的。     

随机模糊信息系统的知识约简

利用模糊逻辑中的R-型蕴涵算子定义随机模糊信息系统对象集上的模糊等价关系,进而实现对随机模糊信息系统知识的近似表示。讨论随机模糊下近似、上近似集的模糊概率与模糊信任测度、模糊似然测度之间的关系。给出基于模糊信任测度和模糊似然测度的随机模糊信息系统知识约简的方法。     

SVD-based complexity reduction of rule-bases with nonlinearantecedent fuzzy sets

With the help of the singular value decomposition (SVD) based complexity reduction method, not only can the redundancy of fuzzy rule-bases be eliminated, but further reduction can also be made, considering the allowable error. Namely, in the case of higher allowable error, the result may be a less complex fuzzy inference system, with a smaller rule-base. This property of the SVD-based reduction method makes possible the usage of fuzzy systems, even in cases when the available time and resources are limited. The original SVD-based reduction method was proposed for rule-bases with linear antecedent fuzzy sets. This limitation remained valid in the later extensions, as well. The purpose of this paper is to give a formal mathematical proof for the original formulas with nonlinear antecedent fuzzy sets and thus to end this limitation     

Adaptation of TS fuzzy models without complexity expansion: HOSVD-based approach

One direction of measured data-set based modeling applies fuzzy logic identification tools and results in a fuzzy rule-base model. A typical problem of fuzzy identification methods is that the complexity of the resulting fuzzy rule-base, namely the number of rules in the rule-base, explodes with the modeling accuracy. As a result, the topic of fuzzy rule-base complexity reduction techniques emerged in the last decade. A common disadvantage of fuzzy rule-base complexity reduction methods is that the resulting complexity minimized fuzzy-rule bases cannot be simply adapted to new information. If we have new information that cannot be described by the fuzzy rules of the complexity minimized fuzzy rule-base, then we have two choices. The first choice is to add new fuzzy rules to the fuzzy rule-base until the new information can be described. The second choice is to modify the new information until it can be described by the fuzzy rule-base without using additional fuzzy rules. This second case has the prominent role if the number of fuzzy rules in the fuzzy rule-base is limited. This paper proposes a method for the second choice. The proposed method minimizes the necessary modification of the new information. This paper focuses attention on a recent complexity reduction method, termed Higher Order Singular Value Decomposition (HOSVD)-based complexity reduction, and Takagi-Sugeno (TS) inference operator-based fuzzy rule-bases. An example is used to provide the validation of the proposed method. In order to demonstrate the effectiveness of the proposed method, a control system of a differential-steered automatic guided vehicle is modeled in the paper.     

Chattering‐ free hybrid adaptive neuro‐fuzzy inference system‐particle swarm optimisation data fusion‐based BG‐level control

In this study, a closed‐loop control scheme is proposed for the glucose–insulin regulatory system in type‐1 diabetic mellitus (T1DM) patients. Some innovative hybrid glucose–insulin regulators have combined artificial intelligence such as fuzzy logic and genetic algorithm with well known Palumbo model to regulate the blood glucose (BG) level in T1DM patients. However, most of these approaches have focused on the glucose reference tracking, and the qualitative of this tracking such as chattering reduction of insulin injection has not been well‐studied. Higher‐order sliding mode (HoSM) controllers have been employed to attenuate the effect of chattering. Owing to the delayed nature and non‐linear property of glucose–insulin mechanism as well as various unmeasurable disturbances, even the HoSM methods are partly successful. In this study, data fusion of adaptive neuro‐fuzzy inference systems optimised by particle swarm optimisation has been presented. The excellent performance of the proposed hybrid controller, i.e. desired BG‐level tracking and chattering reduction in the presence of daily glucose‐level disturbances is verified.Inspec keywords: fuzzy control, variable structure systems, particle swarm optimisation, neurocontrollers, fuzzy neural nets, blood, genetic algorithms, closed loop systems, medical control systems, fuzzy reasoning, diseases, nonlinear control systems, sugarOther keywords: data fusion, adaptive neuro‐fuzzy inference systems, particle swarm optimisation, hybrid controller, desired BG‐level tracking, chattering reduction, daily glucose‐level disturbances, closed‐loop control scheme, glucose–insulin regulatory system, type‐1 diabetic mellitus patients, innovative hybrid glucose–insulin regulators, artificial intelligence, fuzzy logic, genetic algorithm, Palumbo model, blood glucose level, T1DM patients, glucose reference tracking, insulin injection, mode controllers, glucose–insulin mechanism, chattering‐free hybrid adaptive neuro‐fuzzy inference system, particle swarm optimisation data fusion‐based BG‐level control     

e-Commerce and supply chains: Modelling of dynamics through fuzzy enhanced high level petri net

Although information plays a major role in effective functioning of supply chain networks (SCNs), studies that deal specifically with the dynamics of supply chains are few. This problem is relatively new since fast communications and the means to employ it for effective management of supply chains did not exist till recently. In order to provide a vehicle for dynamic modelling and analysis of supply chain operations in vague and uncertain environments, we propose a fuzzy enhanced high level petri net (FEHLPN) model. The proposed model captures the capability of petri nets for graphical and analytical representation of dynamic SCNs with the management of uncertain information provided by fuzzy logic. The dynamics associated with two production planning and control policies are modelled, viz. make-to-stock and assemble-to-order in vague and ambiguous situations in electronic commerce environment. A fuzzy set and fuzzy truth-values are attached to an uncertain fuzzy token to model imprecision and uncertainty. The proposed FEHLPN incorporates essential aspects of rule-based systems, such as conservation of facts, refraction, and closed-world assumption.     

Fuzzy rule interpolation for multidimensional input spaces in determining d50c of hydrocyclones

Fuzzy rule-based systems have been very popular in many engineering applications. In mineral engineering, fuzzy rules are normally constructed using some fuzzy rule extraction techniques to establish the determination model in predicting the d50c of hydrocyclones. However, when generating fuzzy rules from the available information, it may result in a sparse fuzzy rule base. The use of more than one input variable is also common in hydrocyclone data analysis. This paper examines the application of fuzzy interpolation to resolve the problems using sparse fuzzy rule bases, and to perform analysis of fuzzy interpolation in multidimensional input spaces.     

Heuristics for managing trainable binary inspection systems

Binary inspection systems such as those based on ideal templates, neural networks, fuzzy logic, and genetic algorithms are trained by presenting them with exemplars of acceptable work. The system inspects new work by comparing it to the exemplars. The operator may not always agree with the judgment of the system and may decide to retrain it during production. This article explores the quality risks of using and of modifying trainable systems. Risk reduction heuristics used in software development are explored and adapted for use with trainable inspection systems. The use of these heuristics is illustrated in a series of scenarios.     

Fuzzy-logic-based network for complex systems risk assessment: Application to ship performance analysis

In this paper, a new interpretation of intuitionistic fuzzy sets in the advanced framework of the Dempster–Shafer theory of evidence is extended to monitor safety-critical systems’ performance. Not only is the proposed approach more effective, but it also takes into account the fuzzy rules that deal with imperfect knowledge/information and, therefore, is different from the classical Takagi–Sugeno fuzzy system, which assumes that the rule (the knowledge) is perfect. We provide an analytical solution to the practical and important problem of the conceptual probabilistic approach for formal ship safety assessment using the fuzzy set theory that involves uncertainties associated with the reliability input data. Thus, the overall safety of the ship engine is investigated as an object of risk analysis using the fuzzy mapping structure, which considers uncertainty and partial truth in the input–output mapping. The proposed method integrates direct evidence of the frame of discernment and is demonstrated through references to examples where fuzzy set models are informative. These simple applications illustrate how to assess the conflict of sensor information fusion for a sufficient cooling power system of vessels under extreme operation conditions. It was found that propulsion engine safety systems are not only a function of many environmental and operation profiles but are also dynamic and complex.     

A mathematical programming approach to fuzzy queues with batch arrivals

This paper proposes a mathematical programming approach for constructing the membership functions of the performance measures in batch-arrival queueing systems with constant batch size and the arrival rate and service rate being fuzzy numbers. The basic idea underlying the proposed method is to transform a fuzzy batch-arrival queue to a family of conventional crisp queues with batch arrivals by applying the α-cut approach. Then the family of crisp batch-arrival queues is described by formulating a pair of parametric nonlinear programs, through which the membership functions of the performance measures can be derived. A numerical example is solved successfully to demonstrate the validity of the proposed approach. Since the performance measures are completely expressed by membership functions rather than by crisp values, more information is provided for designing queueing systems. The successful extension of batch-arrival queues to fuzzy environments permits queueing models to have wider applications in the real world.     

模糊粗糙集在轴承故障模式识别中的应用

提出一种基于模糊粗糙集理论的模式识别方法,将动态聚类法和方差分析法引入连续属性模糊化,获取模糊隶属函数,避开了粗糙集理论属性离散化过程带来的信息丢失;利用F检验判断分类的合理性,克服了人为确定分类数目的缺点;应用模糊化得到的模糊决策表进行条件属性约简,通过属性值约简,提取了清晰、简明的故障模式规则。轴承故障模式识别结果表明,该方法对比一般粗糙集理论,有效地提高了模式识别精度,在实际模式识别中具有很好的应用价值。