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.     

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.     

边界不确定信息的处理—Fuzzy集和Vague集

Fuzzy集和Vague集都可用于边界不确定信息的处理。文章首先研究了这两种集合间的关系、性质,提出了Fuzzy集和Vague集的边界不确定性都可用犤0,1犦上的一个子区间来进行描述的思想。然后基于一些Vague集相似度量的思想,提出了一种求Fuzzy集和Vague集的相似性度量方法,并建立了基于Fuzzy集和Vague集度量方法的统一模型,该模型在这两种集合相似性度量上是等价的,并给出了该模型满足的性质。     

Fuzzy插值及其Fuzzy泛函网络构造理论

首先分别介绍了泛函网络概念和Fuzzy插值概念及性质；将泛函网络结构特性和Fuzzy插值映射有机地结合起来，提出了一类新型FuzzY泛函网络模型，给出其Fuzzy泛函网络构造方法；采用构造性方法从理论上证明了FuzZy泛函网络能够以任意精度逼近任意定义在有界闭集上的连续函数。这从理论上为Fuzzy泛函网络的使用提供了依据，具有明显的实际应用价值。     

Fuzzy前馈补偿及应用*

本文提出一种由Fuzzy反馈、Fuzzy前馈补偿构成的Fuzzy控制系统,证明了它的存在性,提出实现该Fuzzy控制方法的计算机算法,并将此算法运用于数学模型未知和频繁干扰的石英器皿过程控制系统中,取得令人满意的控制效果。     

Fuzzy控制理论在甜菜制糖过程的应用

基于Fuzzy控制理论提出了归一Fuzzy量化及带修正函数的Fuzzy控制规则自调整 方法,并介绍了该法在甜菜制糖过程的成功应用.通过某糖厂的运行结果,证明了微机Fuzzy 控制在甜菜制糖过程应用的可行性与有效性.     

基于一元Fuzzy事件的大脑存储模型

为改正已有大脑存储模型的缺陷,提出了基于一元Fuzzy(模糊)事件的大脑存储模型.它将外界不同的认知信息融合为Fuzzy认知元的形式,并由一元Fuzzy事件的发生触发存储;存储的机制被定义为由一系列存储子计算构成的存储计算;模型的存储数据是认识,模型的物理存储器是一个容量有限但行列可变的存储体,从而保证有限的存储容量存储尽可能多的认识.实验表明,该模型能够模拟大脑的归纳、记忆、回忆、遗忘等存储功能.因此,基于一元Fuzzy事件的大脑存储模型是一种理想的大脑存储模型.     

关于Fuzzy推理式的可信度估计

众所周知,Fuzzy逻辑比古典逻辑更为充分更为合理地反映了我们的客观世界。在Fuzzy逻辑中,公式的值可取为[0,1]中的任一实数。这一值可看作是该公式的可信度。若S为一公式,则T(S)代表S的可信度,在[1]中,已经对Fuzzy逻辑中的基本子句的归结式的值做了估计:设C_1,C_2为两个Fuzzy逻辑下的基本子句,R(C_1,C_2)为它们的任一归结式.max[T(C_1),T(C_2)]=b,min[T(C_1),T(C_2)]=a>0.5,b≥a,则     

抗生素生产过程补料时机的Fuzzy识别与预报

本文就抗生素发酵生产过程,提出了一种补料时机的Fuzzy识别、预报的建模方法,并根据20m~3发酵罐工业数据的分析,选取了以总糖、总糖变化率、氨基氮和氨基氮变化率四个主要参数作为输入集因素,运用本文提出的方法,建立了一个实际的抗生素发酵生产过程补料时机的 Fuzzy 识别、预报模型。经现场调试效果良好,与富有经验的行家相比,准确率达90%以上。     

覆盖粗糙直觉Fuzzy集模型

考虑到经典粗糙集模型中等价关系过于严格的缺陷和直觉Fuzzy集在处理不确定信息时所具有的表达力,建立了覆盖粗糙直觉Fuzzy集模型,并给出了该模型下的一些性质;接着引入了覆盖粗糙直觉Fuzzy集模型的粗糙度和粗糙熵的概念,讨论其不确定性度量;最后给出了算例。     

FUZZY MEASURES FOR FUZZY SIGNAL-TO-NOISE RATIOS

ABSTRACT

Signal-to-noise ratios (SNRs) are widely applicable in quality engineering for improving product quality. In real-world applications, the observations are sometimes described in linguistic terms, or are only approximately known, rather than equated with randomness. To deal with imprecise data, the notion of fuzziness was introduced. This paper develops a procedure to calculate the SNR with fuzzy observations. The idea is based on the extension principle. A pair of mathematical programs was formulated to calculate the lower and upper bounds of the fuzzy SNR at possibility level α. From different values of α, the membership function of the SNR was approximated. Three different types of SNRs are discussed: “higher the better,” “lower the better,” and “nominal the best.” Because the SNR is expressed by a membership function rather than by a crisp value, more information is provided for making decisions.     

模糊概念的模糊度研究

本文深入研究了模糊理论中有关模糊度的概念、定义和计算方法、分析了它们在合理性、一致性和区别度等方面存在的问题.在此基础上,提出了一种新的模糊度定义和计算的方法,改进了其合理性、一致性和区别度,对于模糊逻辑的研究和发展具有重要的理论和应用意义.     

基于模糊数模糊集的模式识别

讨论了模糊数模糊集的模式识别问题.首先,引入了模糊数的两种排序方法及模糊数模糊集的概念,并以此给出了模糊数fuzzy集的隶属原则;其次,给出了模糊数模糊集的模式识别的实例;最后,为了度量所得结论的可靠程度,引进了可信度的概念,并提出了可信度的计算公式.     

参考模糊集合构造方法及模糊模型辨识

本文提出了一种基于参考模糊集的模糊模型辨识方法,探讨了模型结构和模糊关系的辨识问题.在隶属函数的定义上引入了优化算法,最终将辨识问题转化为优化问题.文中给出了具体的辨识算法,仿真实例表明该模型辨识方法具有满意的精度.     

模糊寻优问题的模糊遗传算法FGA

针对模糊寻优问题，本文基于模糊集的质心概念来确定模糊集的大小，并进而提出了模糊遗传算法ＦＧＡ。算法ＦＧＡ与遗传算法ＧＡ有本制区别，能用效地找到了模糊寻优问题的近似解，本文对算法ＦＧＡ的有效性作了较深入的分析。     

ROUGH FUZZY SETS AND FUZZY ROUGH SETS*

The notion of a rough set introduced by Pawlak has often been compared to that of a fuzzy set, sometimes with a view to prove that one is more general, or, more useful than the other. In this paper we argue that both notions aim to different purposes. Seen this way, it is more natural to try to combine the two models of uncertainty (vagueness and coarseness) rather than to have them compete on the same problems. First, one may think of deriving the upper and lower approximations of a fuzzy set, when a reference scale is coarsened by means of an equivalence relation. We then come close to Caianiello's C-calculus. Shafer's concept of coarsened belief functions also belongs to the same line of thought. Another idea is to turn the equivalence relation into a fuzzy similarity relation, for the modeling of coarseness, as already proposed by Farinas del Cerro and Prade. Instead of using a similarity relation, we can start with fuzzy granules which make a fuzzy partition of the reference scale. The main contribution of the paper is to clarify the difference between fuzzy sets and rough sets, and unify several independent works which deal with similar ideas in different settings or notations.     

一种基于模糊聚类的模糊辨识方法

介绍一种基于模糊聚类的模糊辨识方法。首先利用含有聚类准则函数的模糊聚类方法来确定模糊规则数和模型前提参数,然后利用最小二乘法来辨识模型的结论参数,最后采用梯度下降法来调整模型的参数。该方法应用于Box-Jenkins数据仿真实例,仿真结果表明该方法简单有效。     

FUZZY MEASURES AND FUZZY INTEGRALS: AN OVERVIEW

This paper provides an overview of fuzzy measures, fuzzy integration theories and Choquet's capacity theory. Belief, plausibility, and possibility measures are characterized as Choquet capacities and as fuzzy measures. The relationship between possibility measures, fuzzy sets, and approximate reasoning is established. Recent results on extensions of fuzzy measures, structural characteristics of fuzzy measures, and convergence of function sequences on fuzzy measure spaces are presented. Fuzzy measure integration concepts due to Sugeno and Choquet and their applications are discussed. An extensive list of references to the literature of fuzzy measures, Sugeno and Choquet integrals, fuzzy probabilities, fuzzy random variables, probabilistic sets, and random sets is provided. Applicalions discussed or referenced include information fusion, information retrieval, approximate reasoning, artificial intelligence, uncertainty theory, and control and decision theory.