Extrapolation of fuzzy values from incomplete data bases

This paper presents different approaches which enable a data base management system to obtain a plausible fuzzy estimate for an attribute value of an item for which the information is not explicitly stored in the data base. This can be made either by a kind of analogical reasoning from information about particular items or by means of expert rules which specify the (fuzzy) sets of possible values of the attribute under consideration, for various classes of items. Another kind of expert rules enables the system to compute an estimate from the attribute value of another item provided that, in other respects, this latter item sufficiently resembles the item, the value of which we are interested in; then these expert rules are used either for controlling the analogical reasoning process or for enlarging the scope of application of the first kind of expert rules. The different approaches are discussed in the framework of possibility theory.     

Data mining of resilience indicators

In recent years, the Asia-Pacific region has experienced several financial setbacks, including speculative attacks in 1998 and the SARS outbreak in 2003. Financial stresses of this nature are unanticipated, and not all of the dangers can be predicted by the examination of market information and macroeconomic indicators. The Early Warning System (EWS) that has been adopted by the International Monetary Fund may not be able to predict future financial crises for all possible scenarios, because shocks come in many different forms. To supplement the EWS, this paper proposes a data mining framework to measure the resilience of an economy. The resilience framework does not predict a crisis, but rather assesses the current state of health of an economy and its ability to withstand a financial shock should one occur. The framework is based on a feedback system consisting of two stages. The first stage assigns a resilience score to each economy based on a fuzzy logic scoring scheme that is built on the ambiguous reasoning of experts. The second stage uses the classification tree approach to estimate thresholds for each economic indicator, and examines the quality of the fuzzy score. The result from the second stage is then passed back to the first stage as feedback. The final result is obtained when the feedback system reaches its equilibrium state. The proposed resilience framework is applied to the external-sector and the public-sector economies of several countries to illustrate its applicability.     

Micromechanical characterisation of fibre/matrix interfaces

Theoretical analyses for the single fibre pull-out and push-out models under monotonic loading are given which are based on a shear-lag analysis in a fracture mechanics approach considering non-constant friction at the debonded interface as a result of fibre Poisson contraction (or expansion). The solutions allow the determination of typical fibre/matrix interfacial properties such as the interfacial fracture toughness, G_ic, the coefficient of friction, μ, and the residual clamping stress, q₀. Under cyclic loading the interfacial properties are expected to degrade as a result of repetitive abrasion, and a power law function is assumed between μ and the number of elapsed cycles, N. However, G_ic is assumed to be unaffected and a fracture mechanics based debond criterion is derived for the relationship between the external applied stress, the debond length and the reduced friction coefficient for both fibre pull-out and fibre push-out. In addition, the relative displacements between the free fibre end and the matrix top are obtained for cyclic fatigue when the fibre is loaded and unloaded. A relationship obtained for the protrusion (or intrusion) length in fibre pull-out (or push-out) experiments allows the severity of the interface frictional degradation to be evaluated and characterised. Similarities and differences in the frictional degradation behaviour between fibre pull-out and push-out are also identified.     

A new voltage control equipment using fuzzy inference

Power system control equipment needs higher sensitivity and operational reliability. Advanced voltage control equipment is needed for reducing the frequency of tap changes and improving the characteristics (the relationship between the actual voltage and reference voltage) of the voltage to meet today's power system requirements. However, these objectives are in a trade-off relationship. Studies of voltage control derived from a knowledge base suitable for electric power systems can satisfy these objectives using fuzzy inference. Compared with corresponding conventional equipment, the new equipment improved the deviation of 30 min average voltage of 30 percent. This paper describes the design concept of new voltage control equipment using fuzzy inference. In addition, field test results are described along with rules of fuzzy inference, membership functions, and the deviation of 30 min average voltage through detailed simulation.     

EXTENSION OF THE RELATIONAL DATABASE AND ITS ALGEBRA WITH ROUGH SET TECHNIQUES

This paper describes a database model based on the original rough sets theory. Its rough relations permit the representation of a rough set of tuples not definable in terms of the elementary classes, except through use of lower and upper approximations. The rough relational database model also incorporates indiscernibility in the representation and in all the operators of the rough relational algebra. This indiscernibility is based strictly on equivalence classes which must be defined for every attribute domain. There are several obvious applications for which the rough relational database model can more accurately model an enterprise than does the standard relational model. These include systems involving ambiguous, imprecise, or uncertain data. Retrieval over mismatched domains caused by the merging of one or more applications can be facilitated by the use of indiscernibility, and naive system users can achieve greater recall with the rough relational database. In addition, applications inherently “rough” could be more easily implemented and maintained in the rough relational database.     

投资项目经济评价指标的研究

本文在探讨“平坦”模糊数及其运算的基础上,提出模糊内部收益率的概念;并利用Prade与Dubois提出的比较两个模糊数大小的指标,给出“近似相等”的概念;从而给“模糊内部收益率”的计算提供了一个方法。     

真空水冷铜坩埚感应炉模糊控制专家系统的研究

研究真空水冷铜坩埚感应炉中模糊控制专家系统。阐述该炉控制系统复杂性 ,介绍模糊控制专家系统的主要结构和各主要部分的功能和一些算法     

基于模糊决策的风力发电产业评价研究

根据风力发电技术产业的特点,构建改进层次分析与模糊一致矩阵相结合的方案决策数学模型,该模型既可以减少确定判断矩阵的盲目性,又可以避开评价体系中各等级间的隶属度确定问题,使评价结果更加可靠,适合于信息尚不完整、受主观因素影响较强的评价。实例验证了这一模型的合理性、可行性和有效性,具有实用价值。     

模糊综合评判理论在研究性学习系统中的研究及应用

介绍了工程数学中的模糊综合评判理论以及相应树型向量推理算法,并将模糊综合推理机制运用到研究性学习系统中进行研究型应用。该系统中运用模糊综合评判对学习效果进行合理评判,得出综合成绩,以其作为改进研究性学习模式的参考评证,从而使研究性学习过程良性发展。     

Growing fuzzy topology adaptive resonance theory models with a push-pull learning algorithm

A new incrementally growing neural network model, called the growing fuzzy topology ART (GFTART) model, is proposed based on integrating the conventional fuzzy ART model with the incremental topology-preserving mechanism of the growing cell structure (GCS) model. This is in addition, to a new training algorithm, called the push-pull learning algorithm. The proposed GFTART model has two purposes: First, to reduce the proliferation of incrementally generated nodes in the F2 layer by the conventional fuzzy ART model based on replacing each F2 node with a GCS. Second, to enhance the class-dependent clustering representation ability of the GCS model by including the categorization property of the conventional fuzzy ART model. In addition, the proposed push-pull training algorithm enhances the cluster discriminating property and partially improves the forgetting problem of the training algorithm in the GCS model.