基于最小二乘拟合的模糊隶属函数构建方法

针对当前模糊隶属函数构造方法中存在的问题,提出一种构造模糊隶属函数方法. 采用最小二乘法拟合离散数据来获得隶属函数.为减小拟合误差,采用了3 项措施以达到预期目标. 所构建的隶属函数,对任意输入物理量可直接得到其对应模糊语言变量的隶属度,从而有效避免专家指定隶属度的主观臆断性及不一致性. 该方法简单,求解精度高,具有广泛适用性和较强的应用价值. 仿真结果证实了该方法的有效性.

     

一种新颖隶属度函数的模糊支持向量机

传统的支持向量机(SVM)训练含有外部点或噪音数据时,容易产生过拟合(over-fitting)。通过模糊隶属度函数来降低外部点或被污染数据的选择。本文提出了一种新的核隶属度函数,这种新的隶属度函数不仅依赖于每个样本点到类型中心的距离,还依赖于该样本点最邻近的K个其他样本点的距离。实验结果表明了具有该隶属度函数的模糊支持向量机的有效性。     

一种改进的模糊支持向量机算法

模糊隶属度函数设计是模糊支持向量机中的关键步骤.Lin & Wang提出的基于类中心距离的模糊隶属度设计方法,不能从样本集中有效区分噪声或野值点,而且可能降低支持向量的隶属度.针对上述不足,提出一种改进的隶属度函数设计方法.通过引入一个半径控制因子,充分利用样本间的信息,更加合理地设计样本的模糊隶属度.与基于类中心的隶属度方法相比,该方法在不增加时间复杂度的情况下,通过数值实验表明了方法的优势,大大提高了模糊支持向量机的分类精度.     

一种基于遗传算法的模糊关联规则挖掘方法

文中提出了一种基于遗传算法的生成隶属度函数的方法,该方法通过遗传算法对初始种群进行优化,获得一个适应度较高的隶属度函数编码,然后再根据机场噪声数据的实际标准对优化后得到的隶属度函数进行修正,进而得到梯形分布的隶属度函数编码.最后通过得到的隶属度函数对数据进行模糊化,并采用FP-trees算法生成模糊关联规则.该文针对数量型属性提出了这种方法,它的优点是能够使通过遗传算法得到的较优的隶属度函数更加适用于实际的数据集.     

基于类内超平面的模糊支持向量机

分析基于样本与类中心距离设计模糊支持向量机隶属度函数的缺点,使用类内超平面代替类中心,提出基于样本到超平面距离的隶属度函数设计方法.该方法降低隶属度函数对样本集几何形状的依赖,提高模糊支持向量机的泛化能力.最后数值实验表明,与传统的支持向量机和现有的3种不同隶属度函数的模糊支持向量机相比,新隶属度函数可达到最好的分类效果而且速度快.     

二型模糊系统的规则提取算法

模糊规则提取是建立二型模糊系统需要解决的关键问题.提出一种改进的基于c均值模糊聚类算法(FCM)的二型模糊规则提取方法.该方法借助于二型模糊集主隶属度函数的期望与次隶属度函数值之间的联系,能克服已有算法忽略二型模糊集次隶属度函数对模糊聚类结果的影响.仿真实例表明.该算法能成功地提取二型模糊规则,比FCMV算法具有更好的性能和收敛性.     

正态云隶属度函数确定的FSM方法

隶属度函数及其确定方法的研究具有重要意义。采用正态云模型表示的隶属度函数综合了模糊性和随机性,具有普适性;提出了确定隶属度函数的模糊减法均值聚类(FSM)方法,得到了最优聚类中心和数据的隶属度。实例仿真表明,采用该方法确定了石油钻井中总体积的正态云隶属度函数,解决了隶属度函数难以客观描述和难以确定的问题。     

一种基于隶属函数的相似度计算方法及其应用*

针对在模糊实例检索中传统的基于距离函数相似度算法的复杂性和不精确性等缺点,通过分析隶属函数提出了基于隶属函数的相似度计算方法。该方法根据不同类型的检索需求,构建了针对模糊数字型需求和模糊语义型需求的相似度计算模型;同时考虑了属性权重问题,采用基于相似度离差信息的客观赋权方法将主观权重和客观权重加以组合,以组合权重来计算实例的全局相似度。以汽车发动机的概念设计为例,验证了基于隶属函数的相似度计算方法能对不同类型的需求实现快速而有效的实例检索。     

多阈值模糊互信息图像分割方法

提出了多阈值模糊互信息图像分割新方法。该方法首先将模糊隶属度函数引入到传统互信息量中并定义模糊信息量概念;其次将模糊互信息量用于图像分割时给出具体隶属度函数的构造;最后采用混沌优化法来获得多阈值模糊互信息分割方法的最佳阈值。实验结果表明,提出的多阈值模糊互信息图像分割方法是有效的。     

基于模糊隶属度函数的ICA特征提取和识别

提出了一种基于模糊隶属度函数的独立成分分析图像特征提取和识别方法.该方法首先通过主成分分析等对图像进行预处理,然后通过FastICA算法对图像进行处理,构造特征脸子空间,计算训练样本和待测样本在特征脸子空间中的投影,引入模糊隶属度函数,建立矢量隶属度函数,作为识别分类器进行人脸识别.针对ORL标准人脸数据库上的实验结果表明,该方法具有良好的识别分类能力.     

Fuzzy model based recognition of handwritten numerals

This paper presents the recognition of handwritten Hindi and English numerals by representing them in the form of exponential membership functions which serve as a fuzzy model. The recognition is carried out by modifying the exponential membership functions fitted to the fuzzy sets. These fuzzy sets are derived from features consisting of normalized distances obtained using the Box approach. The membership function is modified by two structural parameters that are estimated by optimizing the entropy subject to the attainment of membership function to unity. The overall recognition rate is found to be 95% for Hindi numerals and 98.4% for English numerals.     

基于隶属度的命名实体自动获取研究

利用维基百科(Wikipedia)和已有命名实体资源,提出维基百科类的隶属度计算方法,通过匹配、计算、过滤、扩展、去噪五个步骤构建出具有较高质量和较大规模的命名实体实例集.在英语维基百科数据上进行实验,结果显示,基于隶属度方法自动获取的人名实例规模较DBpedia抽取出的人名实例规模高出近10倍,通过对不同隶属度区间的抽取实例进行人工检验,发现抽取出的前15000个维基百科类的准确率达到99％左右,能够有效支持命名实体类实例的扩充.     

自适应神经网络模糊推理系统最优参数的研究

模糊规则的提取和隶属度函数的学习是模糊系统设计中重要而困难的问题。自适应神经网络模糊推理系统（ANFIS）能基于数据建模,无须专家经验,自动产生模糊规则和调整隶属度函数。在建立一个初始系统进行训练时,其隶属度函数的类型、隶属度函数的数日以及训练次数都是待定的,这三个参数的选择直接影响系统训练后的效果,它们的确定方法有待研究。该文应用自适应神经网络模糊推理系统的方法对一个典型系统进行建模仿真,并阐述这三个参数的寻优方法。     

Approximation capability of fuzzy systems using translations anddilations of one fixed function as membership functions

This paper reports on a related study on approximation theory of fuzzy systems. First, some basic principles are presented to construct membership functions. Then, an approach is proposed to form membership functions by using translations and dilations of one fixed function (called a basis function) which is very similar to that in wavelets analysis. The properties of this type of membership function reflect the advantages of the given approach. Finally, it is proved that fuzzy systems based on such membership functions are universal approximators under certain mild conditions on the basis function. This conclusion expands the family of fuzzy systems which can be universal approximators     

Towards Behavioral Maude: Behavioral Membership Equational Logic

How can algebraic and coalgebraic specifications be integrated? How can behavioral equivalence be addressed in an algebraic specification language? The hidden-sorted approach, originating in work of Goguen and Meseguer in the early 80's, and further developed into the hidden-sorted logic approach by researchers at Oxford, UC San Diego, and Kanazawa offers some attractive answers, and has been implemented in both BOBJ and CafeOBJ. In this work we investigate both further extensions of hidden logic, and an extension of the Maude specification language called BMaude supporting this extended hidden-sorted semantics.Maude's underlying equational logic, membership equational logic, generalizes and increases the expressive power of many-sorted and order-sorted equational logics. We develop a hidden-sorted extension of membership equational logic, and give conditions under which theories have both an algebraic and a coalgebraic semantics, including final (co-)algebras. We also discuss the language design of BMaude, based on such an extended logic and using categorical notions in and across the different institutions involved. We also explain how Maude's reflective semantics provides a systematic method to extend Maude to BMaude within Maude, including module composition operations, evaluation, and automated proof methods.     

New approaches to relaxed stabilization conditions and H-infinity control designs for T-S fuzzy systems

This paper focuses on the problem of fuzzy control for a class of continuous-time T-S fuzzy systems.New methods of stabilization design and H infinity control are derived based on a relaxed approach in...     

FNN在数据库模糊查询中的研究

提出了一种基于模糊神经网络对数据库模糊查询的新方法,将模糊理论、模糊神经网络与数据库系统相结合。利用FNN直接生成隶属函数,来完成模糊查询,避免了直接指定隶属函数所带来的主观性,并能较好地反映数据的特征。     

A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions

Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. This paper thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. We present a GA-based framework for finding membership functions suitable for mining problems and then use the final best set of membership functions to mine fuzzy association rules. The fitness of each chromosome is evaluated by the number of large 1-itemsets generated from part of the previously proposed fuzzy mining algorithm and by the suitability of the membership functions. Experimental results also show the effectiveness of the framework.     

A fuzzy bi-criteria transportation problem

In this paper, a fuzzy bi-criteria transportation problem is studied. Here, the model concentrates on two criteria: total delivery time and total profit of transportation. The delivery times on links are fuzzy intervals with increasing linear membership functions, whereas the total delivery time on the network is a fuzzy interval with a decreasing linear membership function. On the other hand, the transporting profits on links are fuzzy intervals with decreasing linear membership functions and the total profit of transportation is a fuzzy number with an increasing linear membership function. Supplies and demands are deterministic numbers. A nonlinear programming model considers the problem using the max–min criterion suggested by Bellman and Zadeh. We show that the problem can be simplified into two bi-level programming problems, which are solved very conveniently. A proposed efficient algorithm based on parametric linear programming solves the bi-level problems. To explain the algorithm two illustrative examples are provided, systematically.