基于广义Choquet模糊积分的语言特征对象近似识别/分类

语言特征存在于许多实际应用中，如模糊数知识系统、语义检索、模糊人脸识别等．为解决这类具有语言特征(模糊数)对象的识别／分类问题，提出了基于广义Choquet模糊积分的分类方法与模型．该方法不仅可以解决数值特征对象识别／分类问题．同时为具有模糊数特征的对象识别／分类问题提供了手段，从而扩大了原有分类模型的适用领域．模糊人脸识别模拟实验证明，在语言特征环境下．该分类模型有效地实现了语言模糊对象的近似辨识．     

模糊关系数据库系统FPDB

传统的数据库只能用来处理精确而良构的数据,为了处理模糊数据,我们开发了基于模糊关系模型的模糊数据库系统FPDB(Fuzzy Prolog Database)FPDB是模糊集合论,逻辑程序设计与关系数据库的有机结合,本文介绍了FPDB中的模糊关系模型和模糊查询语言FSQL,FPDB中的模糊关系模型的基础是用于表示模糊信息的可能性分布,该模型可以看作Codd关系模型的拓广,FSQL语言提供了一种结构查询方法,标准SQL是它的特例。     

基于广义模糊基函数的多变量模糊模型及其辨识方法

利用模糊穴位映射理论，提出一种有效描述复杂多变量系统的模糊模型－－广义模糊基函数展开式，它可方便地处理多输入多输出系统的语言和系统信息，并可逼近任意非线性函数，是一种通用的多变量模糊逻辑系统模型。利用语言信息，提出一种新的自适应参数辨识方法－－改进的Ｗｉｄｒｏｗ－Ｈｏｆｆ学习规则，仿真结果验证了它的有效性。     

模糊关系数据库查询语言FSQL

模糊数据库是模糊信息处理系统的重要组成部分。本文以ＳＱＬ语言为基础，设计了模糊关系数据库查询语言ＦＳＱＬ。ＦＳＱＬ语言采用了模糊值模糊关系数据模型，提供了相应的模糊数据定义与模糊数据操纵功能。为了便于模糊信息的表示和管理，ＦＳＱＬ语言增加了模糊数据类型，如简单标量型、模糊标量型、简单数集、模糊数集等。另外，为了便于模糊查询，扩充了模糊比较库函数及自定义隶属函数。     

多功能感知系统中的面向Agent技术

本文将人类语言分为自然语言和人体语言，阐述了人体语言的概念，讨论了人类语言的结构与包容关系，归纳了人体语言与自然语言的信息融合模型．在该模型的基础上，利用面向Ａｇｅｎｔ技术设计了一个多功能感知系统的框架结构，提出了Ａｇｅｎｔ关系图表示方法，并具体实现了一种人体语言感知原型系统．     

模糊逻辑系统与支持向量机的关系探索

研究了模糊逻辑系统和支持向量机的关系，指出模糊逻辑系统是以峰点作为支持向量，以隶属函数作为基函数的推理系统．模糊逻辑系统是一种特殊的支持向量机。文中提出了一种基于模糊规则的支持向量机控制模型，仿真结果表明了这种模型的可行性和有效性。     

基于正交最小二乘估计的非线性系统模糊辨识

提出了一种基于正交最小二乘的模糊模型结构和参数辨识方法．首先，基于正交最小二乘方法分析模糊模型的模糊关系矩阵．通过分析正交向量在模型中贡献的大小，确定模糊模型的结构，即确定模糊模型的规则数、规则．另外，再次通过正交最小二乘方法确定模糊模型的结论参数，实现模糊模型结构和参数的优化．为了证明该方法的有效性，采用该文方法对Box-Jenkins煤气炉数据系统进行建模研究，仿真结果表明该文方法能够对非线性系统进行辨识．     

基于模糊神经网络的非线性系统模型的辨识

该文提出一种非线性系统的模型辨识方法．利用关系聚类法来进行结构辨识，从而自动获得模糊规则库，并可以得到模糊系统的初始参数，在聚类的基础上，构造一个与之相匹配的模糊神经网络，用它的学习算法来训练网络，得到一个精确的模糊模型，从而实现参数辨识，通过对两个非线性系统辨识的仿真结果验证了该方法的有效性。     

模糊仿真─—定性模型的建立与辨识

本文的目的是在对定性仿真理论进行回顾的基础上，说明模糊方法在对复杂系统进行定性仿真中的合理性。本文首先给出了在仿真建模中应用模糊集理论的基本方法，然后讨论了模糊仿真的两个应用方向，即采用语言变量的模糊仿真和通过模糊仿真来辨识定性模型。     

模糊代数系统与模糊范式

模糊代数系统是研究模糊语言及模糊自动机的有力工具,语言的范式已有详细的研究。在模糊代数系统的基础上,给出模糊范式的概念并进行分类,研究proper模糊代数系统与Chomsky模糊范式的关系,给出由proper模糊代数系统构造Chomsky模糊范式的方法,这种方法使得proper模糊代数系统在表达形式上规范化。为了研究proper模糊代数系统的解,构造了operator模糊范式,并得到结论:二者强解的第一分量相等,其余对应分量相差一个右逆算子。模糊范式提供了研究模糊代数系统的不同视角,彰显了模糊范畴下代数系统比经典代数系统具有更好的性质。     

模糊控制系统的闭环模型及稳定性分析

本文在分析模糊控制系统推理机制的基础上,给出了模糊控制系统的闭环分析模型,并利 用其模糊关系矩阵,在模糊集合理论基础上,提出了模糊闭环控制系统稳定的充分和必要条 件.     

A group decision-making model based on incomplete comparative expressions with hesitant linguistic terms

As a result of uncertainty and complexity for environments of decision-making, it is more suitable for decision makers to use hesitant fuzzy linguistic information. In this paper, a novel group decision making (GDM) model based on fuzzy linear programming is proposed for incomplete comparative expressions with hesitant fuzzy linguistic term set (HFLTSs). We establish an equivalence theorem of additive consistency between 2-tuple fuzzy linguistic preference relation (FLPR) and corresponding fuzzy preference relation. Based on this framework, a fuzzy linear programming is established to address incomplete comparative expressions with HFLTSs. It is more important that the proposed fuzzy linear programming has a double action, finding the highest consistent incomplete 2-tuple FLPR and increasing inconsistent 2-tuple FLPR to the additive consistent 2-tuple FLPR based on given incomplete comparative expressions with HFLTSs. By this means, a novel GDM model is constructed based on importance induced ordered weighted averaging operator. Finally, an investment decision-making in real-world is solved by the proposed model, which shows the result of GDM is effectiveness.     

基于优序关系的犹豫模糊语言多准则决策方法

犹豫模糊语言集是语言集和犹豫模糊集的扩展,受传统Electre方法的启发,构建基于优序关系的犹豫模糊语言多准则决策方法. 首先,给出犹豫模糊语言数的Hausdorff距离公式;然后,基于每一准则下方案评价的对比,建立犹豫模糊语言数的优序关系,并在此基础上,提出一种基于优序关系的犹豫模糊语言多准则决策方法;最后,通过算例表明了所提出方法的有效性和可行性.     

Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures

Linguistic fuzzy modelling, developed by linguistic fuzzy rule-based systems, allows us to deal with the modelling of systems by building a linguistic model which could become interpretable by human beings. Linguistic fuzzy modelling comes with two contradictory requirements: interpretability and accuracy. In recent years the interest of researchers in obtaining more interpretable linguistic fuzzy models has grown.Whereas the measures of accuracy are straightforward and well-known, interpretability measures are difficult to define since interpretability depends on several factors; mainly the model structure, the number of rules, the number of features, the number of linguistic terms, the shape of the fuzzy sets, etc. Moreover, due to the subjectivity of the concept the choice of appropriate interpretability measures is still an open problem.In this paper, we present an overview of the proposed interpretability measures and techniques for obtaining more interpretable linguistic fuzzy rule-based systems. To this end, we will propose a taxonomy based on a double axis: “Complexity versus semantic interpretability” considering the two main kinds of measures; and “rule base versus fuzzy partitions” considering the different components of the knowledge base to which both kinds of measures can be applied. The main aim is to provide a well established framework in order to facilitate a better understanding of the topic and well founded future works.     

Theoretical and linguistic aspects of the fuzzy logic controller

Successful applications of the fuzzy logic controller by various researchers to a variety of ill-defined processes motivated this theoretical study of the fuzzy logic controller. Initially the controller is analysed by traditional (nonlinear) algebraic methods which are particularly useful in stability studies, provided the process is algebraically modelled. Despite the success of this technique, it suffers from a major limitation in that the algebraic model of the controller cannot directly deal with the linguistic aspects of the fuzzy logic controller. This observation leads to the introduction of a more concise, and hence more powerful, notation for representing the linguistic rules that describe the fuzzy logic controller. The so-called linguistic models that arise from this notation are shown to be extremely useful for modelling highly nonlinear low-order systems and for determining, explicitly, the rules of ‘optimal’ fuzzy logic controllers.     

Fuzzy Logic for Biological and Agricultural Systems

Fuzzy logic is a powerful concept for handling non-linear, time-varying, adaptive systems. It permits the use of linguistic values of variables and imprecise relationships for modeling system behavior. The paper presents an overview of fuzzy logic modeling techniques, its applications to biological and agricultural systems and an example showing the steps of constructing a fuzzy logic model.     

基于向量Lyapunov函数方法的模糊控制系统稳定性分析和设计

采用模糊动态模型对连续时间非线性系统进行模糊控制,对闭环模糊系统的稳定性进行分析,并给出系统化的控制器设计程序,在一系列局部模型通过模糊隶属函数连接得到的连续的全局模型中,全面考虑其它关联子系统对标称线性系统的摄动,并利用向量Lyapunov函数的概念和方法,得到了闭环模糊系统稳定的充分条件;仿真例子验证了该设计方法的正确性。     

Assessment of traffic congestion with ORESTE method under double hierarchy hesitant fuzzy linguistic environment

With the new generation of information technology development and the promotion of the Internet, local governments turn their attention to the construction of intelligent transportation systems. More and more cities began building intelligent transportation which has been widely used to monitor urban traffic. Experts can evaluate urban traffic congestion based on the information collected from the big data of intelligent transportation. In recent two years, double hierarchy hesitant fuzzy linguistic term set has been widely used to depict explicit evaluation information, which is straightforward and broad-spectrum. When evaluating traffic congestion in a city, decision makers can utilize double hierarchy hesitant fuzzy linguistic term sets to express vague information. Moreover, the ORESTE method is an applicative method which can select a reliable alternative by subdividing alternatives and reduce the loss of information in the conversion process. In this paper, we propose a double hierarchy hesitant fuzzy linguistic ORESTE method and a new score function of double hierarchy hesitant fuzzy linguistic term set. The method raises a new perspective to reduce the error from other methods and the new score function derives a robust decision-making result. Then, we apply the double hierarchy hesitant fuzzy linguistic ORESTE method to solve a practical case involving choosing the congested city by evaluating the 5S traffic congestion model. Finally, we compare the double hierarchy hesitant fuzzy linguistic ORESTE method with other methods such as the classical ORESTE method and the double hierarchy hesitant fuzzy linguistic MULTIMOORA to illustrate the advantages of our method.     

利用模糊神经网络实现数值信息与语言信息的融合

提出一种数值信息与语言信息融合的实现方法,融合是通过一个模糊神经网络完成的。该方法可用于对既有语言型变量,又有数值型变量的系统建立模型。实现融合的关键是对语言变量定义隶属函数。给出一种考虑决策者的偏好来描述语言变量的隶属函数的方法,仿真结果验证了算法的有效性。     

Some interesting properties of the fuzzy linguistic model based on discrete fuzzy numbers to manage hesitant fuzzy linguistic information

The management of hesitant fuzzy information is a topic of special interest in fuzzy decision making. In this paper, we focus on the use and properties of the fuzzy linguistic modelling based on discrete fuzzy numbers to manage hesitant fuzzy linguistic information. Among these properties, we can highlight the existence of aggregation functions with no need of transformations or the possibility of a greater flexibilization of the opinions of the experts, even using different linguistic chains (multigranularity). Furthermore, based on these properties we perform a comparison between this model and the one based on hesitant fuzzy linguistic term sets, showing the advantages of the former with respect to the latter. Finally, a fuzzy decision making model based on discrete fuzzy numbers is proposed.