A CASE-BASED DECISION SUPPORT SYSTEM FOR INDIVIDUAL STRESS DIAGNOSIS USING FUZZY SIMILARITY MATCHING

Stress diagnosis based on finger temperature (FT) signals is receiving increasing interest in the psycho-physiological domain. However, in practice, it is difficult and tedious for a clinician and particularly less experienced clinicians to understand, interpret, and analyze complex, lengthy sequential measurements to make a diagnosis and treatment plan. The paper presents a case-based decision support system to assist clinicians in performing such tasks. Case-based reasoning (CBR) is applied as the main methodology to facilitate experience reuse and decision explanation by retrieving previous similar temperature profiles. Further fuzzy techniques are also employed and incorporated into the CBR system to handle vagueness, uncertainty inherently existing in clinicians reasoning as well as imprecision of feature values. Thirty-nine time series from 24 patients have been used to evaluate the approach (matching algorithms) and an expert has ranked and estimated similarity. On average goodness-of-fit for the fuzzy matching algorithm is 90% in ranking and 81% in similarity estimation that shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with CBR is a valuable approach in domains, where the fuzzy matching model similarity and case preference is consistent with the views of domain expert. This combination is also valuable, where domain experts are aware that the crisp values they use have a possibility distribution that can be estimated by the expert and is used when experienced experts reason about similarity. This is the case in the psycho-physiological domain and experienced experts can estimate this distribution of feature values and use them in their reasoning and explanation process.     

一种新的基于综合相似度的区间值模糊推理方法

在深入分析已有的基于相似度的模糊推理算法的基础上,针对其计算复杂、未考虑规则前件权重及规则激活方法可能导致结论不全面的问题,提出一种基于综合相似度的区间值模糊推理算法。引入综合相似度、规则阈值、规则可信度等概念,给出新的模糊规则激活方法及推理结论可信度的计算方法。通过算例说明了新方法在多重多维模糊推理的情况下具有还原性,且计算简单,适用于实际应用。     

一种关于布尔模式的相似性度量及其应用

本文提出了一种关于布尔模式的相似性度量，以此为基础，构造了两种聚类算法。这两种算法的共同特征计算简单，并行性强，便于硬件实现，但对于变化是敏感的，其一具有较高的执行效率，其二具有较高的分类精度。     

SUPPORTING AN AUTONOMOUS SOCIAL AGENT WITHIN A COMPETITIVE ENVIRONMENT

We describe how adversarial risk analysis may be used to support the decision making of an autonomous agent that needs to interact with other agents and persons within a competitive environment. We propose several contextualizations for the problem and suggest the conceptual solution in the proposed scenarios.     

应用Fuzzy集合理论测辨系统Fuzzy模型的新方法——Fuzzy推理合成法

本文基于Fuzzy集合理论,根据待辨识系统的量测数据,应用Fuzzy推理合成规则,提出建立系统Fuzzy模型的新方法,通过建立水泥回转窑热工过程Fuzzy模型,证实了该方法的可行性.     

ON THE INTERPRETATION AND CHAINING OF FUZZY IF-THEN RULE BASES USING FUZZY EQUALITY INDICATORS

An interpretation of fuzzy IF-THEN rule bases using certain classes of indicators for fuzzified set inclusion and set equality is defined. Some properties of this interpretation and the chaining of fuzzy IF-THEN rule bases are investigated from a theoretical point of view.     

THE FUZZY GENERALIZED OWA OPERATOR AND ITS APPLICATION IN STRATEGIC DECISION MAKING

We present the fuzzy generalized ordered weighted averaging (FGOWA) operator. It is an extension of the GOWA operator for uncertain situations where the available information is given in the form of fuzzy numbers. This generalization includes a wide range of mean operators such as the fuzzy average (FA), the fuzzy OWA (FOWA), and the fuzzy generalized mean (FGM). We also develop a further generalization by using quasi-arithmetic means that we call the quasi-FOWA operator. The article ends with an illustrative example where we apply the new approach in the selection of strategies.     

A comparative study on consensus measures in group decision making

Decision situations in which several individual are involved are known as group decision‐making (GDM) problems. In such problems, each member of the group, recognizing the existence of a common problem, tries to come to a collective decision. A high level of consensus among experts is needed before reaching a solution. It is customary to construct consensus measures by using similarity functions to quantify the closeness of experts preferences. The use of a metric that describes the distance between experts preferences allows the definition of similarity functions. Different distance functions have been proposed in order to implement consensus measures. This paper examines how the use of different aggregation operators affects the level of consensus achieved by experts through different distance functions, once the number of experts has been established in the GDM problem. In this situation, the experimental study performed establishes that the speed of the consensus process is significantly affected by the use of diverse aggregation operators and distance functions. Several decision support rules that can be useful in controlling the convergence speed of the consensus process are also derived.     

A NEW E‐LEARNING ACHIEVEMENT EVALUATION MODEL BASED ON ROUGH SET AND SIMILARITY FILTER

The trend of utilizing information and Internet technologies as teaching and learning tools is rapidly expanding into education. E‐learning is one of the most popular learning environments in the information era. The Internet enables students to learn without limitations of space and time. Furthermore, the learners can repeatedly review the context of a course without the barrier of distance. Recently, student‐centered instruction has become the primary trend in education, and the e‐learning system, which is considered with regard to of personalization and adaptability, is more and more popular. By means of e‐learning systems, teachers can adjust the learning schedule instantly for each learner according to a student's achievements and build more adaptive learning environments. Sometimes, teachers give biased assessments of students’ achievements under uncontrollable conditions (i.e., tiredness, preference) and are in dire need of overcoming this predicament. To solve the drawback mentioned, a new model to evaluate learning achievements based on rough set and similarity filter is proposed. The proposed model includes four facets: (1) select important features (attributes) to enhance classification performance by feature selection methods; (2) utilize minimal entropy principle approach (MEPA) to fuzzify the quantitative data; (3) select linguistic values for each feature and delete inconsistent data using the similarity threshold (similarity filter); and (4) generate rules based on rough set theory (RST). The practical e‐learning achievement data sets are collected by an e‐learning online examination system from a university in Taiwan. To verify our model, the performances of the proposed model are compared with the listing models. Results of this study demonstrate that the proposed model outperforms the listing models.     

智能型多方案综合评价支持系统的设计与实现

本文提出了一个智能型多方案综合评价支持系统的基本结构框架；描述了多目标多层次模糊综合评价的数学模型；讨论了ＩＡＣＥＳＳ的主要实现方法与技术。整个ＩＡＣＥＳＳ已在ＩＢＭ ＰＣ机上实现。     

PREDICTING THE IMPACT OF ANTICIPATORY ACTION ON U.S. STOCK MARKET—AN EVENT STUDY USING ANFIS (A NEURAL FUZZY MODEL)

In this study, the adaptive neural fuzzy inference system (ANFIS), a hybrid fuzzy neural network, is adopted to predict the actions of the investors (when and whether they buy or sell) in a stock market in anticipation of an event–changes in interest rate, announcement of its earnings by a major corporation in the industry, or the outcome of a political election for example. Generally, the model is relatively more successful in predicting when the investors take actions than what actions they take and the extent of their activities. The findings do demonstrate the learning and predicting potential of the ANFIS model in financial applications, but at the same time, suggest that some of the market behaviors are too complex to be predictable.     

A novel risk attitudinal ranking method for intuitionistic fuzzy values and application to MADM

The ranking of intuitionistic fuzzy sets (IFSs) is very important for the intuitionistic fuzzy decision making. The aim of this paper is to propose a new risk attitudinal ranking method of IFSs and apply to multi-attribute decision making (MADM) with incomplete weight information. Motivated by technique for order preference by similarity to ideal solution (TOPSIS), we utilize the closeness degree to characterize the amount of information according to the geometrical representation of an IFS. The area of triangle is calculated to measure the reliability of information. It is proved that the closeness degree and the triangle area just form an interval. Thereby, a new lexicographical method is proposed based on the intervals for ranking the intuitionistic fuzzy values (IFVs). Furthermore, considered the risk attitude of decision maker sufficiently, a novel risk attitudinal ranking measure is developed to rank the IFVs on the basis of the continuous ordered weighted average (C-OWA) operator and this interval. Through maximizing the closeness degrees of alternatives, we construct a multi-objective fractional programming model which is transformed into a linear program. Thus, the attribute weights are derived objectively by solving this linear program. Then, a new method is put forward for MADM with IFVs and incomplete weight information. Finally, an example analysis of a teacher selection is given to verify the effectiveness and practicability of the proposed method.     

PATTERN-BASED LEARNING AND SPATIALLY ORIENTED CONCEPT FORMATION IN A MULTI-AGENT, DECISION-MAKING EXPERT

As they gain expertise in problem solving, people increasingly rely on patterns and spatially oriented reasoning. This paper describes an associative visual-pattern classifier and the automated acquisition of new, spatially oriented reasoning agents that simulate such behavior. They are incorporated into a multi-agent game-learning program whose architecture robustly combines agents with conflicting perspectives. When tested on three games, the visual-pattern classifier learns meaningful patterns, and the pattern-based, spatially oriented agents generalized from these patterns are generally correct. The accuracy of the contribution of each of the newly created agents to the decision-making process is measured against an expert opponent, and a perceptron-Iike algorithm is used to learn game-specific weights for these agents. Much of the knowledge encapsulated by the new agents was previously inexpressible in the program's representation and in some cases is not readily deducible from the rules.     

A rough set-based interval-valued intuitionistic fuzzy conceptual design decision approach with considering diverse customer preference distribution

Conceptual design evaluation plays a crucial role in new product development (NPD) and determines the quality of downstream design activities. Currently, most existing methods focus on fuzzy quantitative the evaluation information of multi-objectives in conceptual schemes selection. However, the above process ignores the various customers' preferences for each scheme under the evaluation objective, causing inconsistent preference weights in the various schemes, which cannot guarantee the market value of the optimal scheme. Furthermore, the ambiguous attitude from experts in the early design stage is not well taken into account. To this end, a conceptual scheme decision model with considering diverse customer preference distribution based on interval-valued intuitionistic fuzzy set (IVIFS) is proposed. The model is divided into three parts. Firstly, the initial decision matrix of multi-experts concerning the qualitative and quantitative design attributes is constructed based on intuitionistic fuzzy sets, and then the IFS decision matrix with interval boundaries is formed by using rough set technology. Secondly, the mapping model of design attribute to customer preference is constructed, and then the demand preference strategy implied by design attribute is judged. Thirdly, based on the demand preference strategy, the preferences’ weights for each scheme are calculated. Next, integrating the evaluation data with the same preference in the scheme, the comprehensive satisfaction of the scheme is obtained through IVIFS weighted aggregation operator, and then the optimal scheme is decided. Eventually, a case study of mobile phone form feature schemes is further employed to verify the proposed decision model, and results are sensitivity analyzed and compared.