一类关于正规三角函数基的标准模糊系统逼近误差公式的讨论

在标准模糊系统的基础上提出了以正规三角函数为基函数的一类模糊系统.通过采用数值分析中的余项与辅助函数方法,对该类模糊系统进行了逼近误差精度的分析,给出了从SISO到MISO的误差界公式.最后,指出了这些公式在模糊系统的理论研究与实际应用的意义.     

利用模糊系统求解非线性Fredholm-Ⅱ积分方程

提出了一种利用模糊系统求解非线性Fredholm-Ⅱ积分方程解析解的方法:首先将积分方程转化为模糊系统,然后利用具有紧支集和正规性的尺度函数构造模糊基函数和积分方程的模糊解,最后构造能量误差函数,通过最小化误差函数学习模糊系统的参数.数值实验表明:用模糊系统求得的便于运算的解析解比用Galerkin方法求得的数值解精度高.     

基于模糊结构元的模糊级数

在文献[1]中提出的模糊结构元概念及文献[4]中的得到的[-1,1]上同序标准单调有界函数类与有界模糊数空间同胚性质基础上,本文给出了基于模糊结构元的模糊数项级数和模糊值函数项级数定义,对其重要性质进行了讨论.     

模糊结构元诱导的两类加权模糊数度量

针对模糊数度量中不同隶属程度对度量的贡献程度应不同的客观事实,给出两类模糊数的结构元加权度量。首先,在区间[-1,1]上的同序标准单调有界函数类B[-1,1]上定义两类结构元加权度量dH、dp,分别讨论了这两类度量空间的完备性和可分性;其次,利用正则模糊结构元导出的模糊泛函,给出一种由B[-1,1]上度量诱导有界闭模糊数全体上的度量方法,进而给出由dH、dp诱导的两类模糊数结构元加权度量dNH、dNp,并分析了两类诱导的模糊数度量空间的完备性和可分性;最后,给出了dNH、dNp与传统方法定义的模糊数度量的区别与联系。     

模糊系统的函数逼近特性研究

模糊系统的函数逼近理论是模糊系统理论的一个重要支柱。自从Ｗａｎｇ（１９９２）首先基于一类特殊系统,提出这一理论以来,很多学者作了进一步的研究。本文提出了一类较为一般的模糊系统,圾限覆盖定理,对这一理论,提出了一种新的并且简单护理一,通过对一般模糊系统的分析研究,指出了模糊系统在函数逼近方面的本质特性及局限性。     

模糊值函数的曲线积分

针对扩张原理在模糊值函数曲线积分中的遍历性问题,根据模糊结构元理论给出了第一型模糊值函数曲线积分的定义.然后通过讨论平面模糊矢量的投影问题,给出了第二型模糊值函数曲线积分定义并对其相关性质进行了讨论.研究结果不仅丰富了模糊分析学理论,而且为具有不确定性因素的工程实践提供了方法依据.     

具有函数的泛逼近性能的模糊控制器的设计

构造出9类具有函数的泛逼近性能的模糊控制器,这些模糊控制器均由模糊蕴涵算子构造而成.利用倒车仿真说明采用具有函数的泛逼近性能的模糊控制器可以用于实际的模糊控制系统中.     

多种模糊值函数微分定义的统一表述

针对模糊值函数微分有多种定义,并且在形式难以得到统一的现状,提出了模糊数的广义限定性运算.在此基础上,利用[-1,1]上同序标准单调函数类与模糊实数空间的同胚性质,给出了广义限定差意义下的模糊值函数微分定义,并证明了这个定义与借助于扩张原理形式、借助于Hukuhara差形式和借助于模糊结构元形式的三种模糊值函数微分定义是等价的,进而得到了基于模糊结构元方法的模糊值函数微分定义的统一表述.     

基于输入-输出数据的重心法模糊系统及其泛逼近性

研究基于输入-输出数据的重心法模糊系统及其泛逼近性.首先,将一组单输入-单榆出数据转换成模糊推理规则,通过选择适当的模糊蕴涵算子生成模糊关系,再利用这种模糊关系求出二雄随机变量的联合概率密度函数.当将模糊蕴涵分别取为Larsen蕴涵和Mamdani蕴涵时,分别得到了两种具体的概率密度函数(称之为Larsen分布和Mamdani分布).其次,利用这两种概率分布.分别求出了对应的两种回归函数,指出这种回归函数实际上是模糊控制中的重心法模糊系统.我们分别给出了这种模糊系统具有泛逼近性的充分条件.从而进一步揭示了模糊系统的概率论意义.     

关于G-V模糊拟阵的对偶模糊拟阵概念的推广

模糊拟阵的研究方法之一就是通过基本序列和导出拟阵序列将模糊拟阵问题转化为普通拟阵问题来进行研究。本文正是采用这个研究方法,主要完成了三项工作:一是给出并证明了闭正规模糊拟阵和正规模糊拟阵的几个充要条件;二是将对偶模糊拟阵概念从闭正规模糊拟阵推广到正规模糊拟阵并讨论了有关性质和计算;三是证明了除正规模糊拟阵外,其他模糊拟阵不存在这样的对偶模糊拟阵。     

Approximation by fuzzy B-spline series

We study properties concerning approximation of fuzzy-number-valued functions by fuzzy B-spline series. Error bounds in approximation by fuzzy B-spine series are obtained in terms of the modulus of continuity. Particularly simple error bounds are obtained for fuzzy splines of Schoenberg type. We compare fuzzy B-spline series with existing fuzzy concepts of splines.     

Type-2 T–S fuzzy impulsive control of nonlinear systems

A novel impulsive control approach based on interval Type-2 T–S fuzzy model has been presented for nonlinear systems in this paper. This approach makes up for the drawback of Type-1 fuzzy impulsive control, which cannot fully handle the uncertainties in describing the complex nonlinear systems by Type-1 fuzzy membership functions and cannot give rigorous fuzzy rules. Further more, this approach uses the “broad band” effect of the Type-2 membership functions to solve the noise of training data and exterior disturbance of the Type-1 fuzzy impulsive control. By using Lyapunov theory and Lipschitz condition, which is combined with integrated approaches such as comparison methods and linear matrix inequalities, the Type-2 fuzzy impulsive controller is designed and the general asymptotical stability analysis of the systems is given. Finally, the simulation of the inverted pendulum model demonstrates the validity and superiority of the proposed method by easily determining the membership functions and choosing minimum number of fuzzy rules and the method can handle random disturbance and data uncertainties very well.     

A measure-theoretic axiomatization of fuzzy sets

Using measurement theory, this paper examines three empirical structures that underlie the representation of fuzzy sets: the fuzzy membership structure, the fuzzy component structure, and the fuzzy system structure. These qualitative structures justify the use of the standard min-max system to represent fuzzy sets. The results of this study facilitate the development of a sound measurement-theoretic axiomatization of fuzzy systems.     

多层前向正则模糊神经网络的逼近能力

Abstract. Four-layer feedforward regular fuzzy neural networks are constructed. Universal ap-proximations to some continuous fuzzy functions defined on (R)“ by the four-layer fuzzyneural networks are shown. At first,multivariate Bernstein polynomials associated with fuzzyvalued functions are empolyed to approximate continuous fuzzy valued functions defined on eachcompact set of R“. Secondly,by introducing cut-preserving fuzzy mapping,the equivalent condi-tions for continuous fuzzy functions that can be arbitrarily closely approximated by regular fuzzyneural networks are shown. Finally a few of sufficient and necessary conditions for characteriz-ing approximation capabilities of regular fuzzy neural networks are obtained. And some concretefuzzy functions demonstrate our conclusions.     

模糊数排序的一种新方法

给出了模糊数排序的一种新方法,并且详细研究了它的一些性质.该方法不仅可以对隶属函数为三角形、梯形等较特殊形式的模糊数进行排序,而且还可以比较隶属函数为多个分段单调函数的模糊数,同时它也考虑了决策者的风险态度.最后进行了算例分析.     

模糊值函数分析学的结构元方法简介(Ⅱ)——模糊值函数及微积分的结构元表述

介绍了利用模糊结构元的模糊值函数的解析表达形式及隶属函数确定,以及基于结构元表示的模糊值函数的微分与黎曼积分的定义、计算与部分性质.同时介绍了模糊值函数拟合的基本思想.     

Improving the accuracy while preserving the interpretability of fuzzy function approximators by means of multi-objective evolutionary algorithms

The identification of a model is one of the key issues in the field of fuzzy system modeling and function approximation theory. An important characteristic that distinguishes fuzzy systems from other techniques in this area is their transparency and interpretability. Especially in the construction of a fuzzy system from a set of given training examples, little attention has been paid to the analysis of the trade-off between complexity and accuracy maintaining the interpretability of the final fuzzy system. In this paper a multi-objective evolutionary approach is proposed to determine a Pareto-optimum set of fuzzy systems with different compromises between their accuracy and complexity. In particular, two fundamental and competing objectives concerning fuzzy system modeling are addressed: fuzzy rule parameter optimization and the identification of system structure (i.e. the number of membership functions and fuzzy rules), taking always in mind the transparency of the obtained system. Another key aspect of the algorithm presented in this work is the use of some new expert evolutionary operators, specifically designed for the problem of fuzzy function approximation, that try to avoid the generation of worse solutions in order to accelerate the convergence of the algorithm.     

H∞ estimation for fuzzy membership function optimization

Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a specific shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a few variables and the membership optimization problem can be reduced to a parameter optimization problem. The parameter optimization problem can then be formulated as a nonlinear filtering problem. In this paper we solve the nonlinear filtering problem using H_∞ state estimation theory. However, the membership functions that result from this approach are not (in general) sum normal. That is, the membership function values do not add up to one at each point in the domain. We therefore modify the H_∞ filter with the addition of state constraints so that the resulting membership functions are sum normal. Sum normality may be desirable not only for its intuitive appeal but also for computational reasons in the real time implementation of fuzzy logic systems. The methods proposed in this paper are illustrated on a fuzzy automotive cruise controller and compared to Kalman filtering based optimization.