应用模糊支持向量机进行英文情感分类

针对英文情感分类问题,对不同样本采用不同权重,通过引入模糊隶属度函数,通过计算样本模糊隶属度确定样本隶属某一类程度的模糊支持向量机分类算法,通过对比选取不同核函数和不同惩罚系数的结果.仿真实验结果表明应用模糊支持向量机进行英文情感分类具有较好的分类能力和较高的识别能力.     

基于模糊熵的加权可变模糊评价方法及其应用北大核心

可变模糊评价的实质是多模型组合评价.在分析了可变模糊评价法的基础上,针对方法存在的不足,提出了一种考虑模型权重的可变模糊评价方法.方法首先利用可变模糊评价模型对样本进行计算,得到各样本在不同模型下的综合相对隶属度;然后基于得到的综合相对隶属度数据,利用模糊熵确定四种模型的重要性排序,结合二元比较模糊决策分析法求得四种模型的权重,进而得到最终综合级别特征值,并以此作为评价依据;最后,以煤与瓦斯突出危险性评价为例进行了方法的验证.结果表明:改进方法可行、有效,评价结果更符合客观实际.     

基于模糊熵的加权可变模糊评价方法及其应用

可变模糊评价的实质是多模型组合评价.在分析了可变模糊评价法的基础上,针对方法存在的不足,提出了一种考虑模型权重的可变模糊评价方法.方法首先利用可变模糊评价模型对样本进行计算,得到各样本在不同模型下的综合相对隶属度;然后基于得到的综合相对隶属度数据,利用模糊熵确定四种模型的重要性排序,结合二元比较模糊决策分析法求得四种模型的权重,进而得到最终综合级别特征值,并以此作为评价依据;最后,以煤与瓦斯突出危险性评价为例进行了方法的验证.结果表明:改进方法可行、有效,评价结果更符合客观实际.     

基于贝塞尔曲线理论的备件需求模糊隶属度函数构建模型

用模糊理论描述备件需求是一种科学适用的方法,针对现有模糊变量隶属度函数构建方法的不足, 设计了基于贝塞尔曲线理论的备件需求模糊隶属度函数构建方法,给出了隶属度求解算法,分析了使拟合误差最小的控制点选择方法.同时通过实例验证以及与最小二乘法的对比分析,验证了贝塞尔曲线方法在构建备件需求模糊隶属度函数方面的有效性.此方法无需事先假设隶属度函数的形态,简单易用、使用灵活并且精度较高.     

煤矿瓦斯风险评估中的多值逻辑

煤矿瓦斯风险评估中因风险状态渐变连续,所以单风险隶属度是可取[0,1]区间上一切实数,可表征"部分属于"模糊状态的模糊隶属度.因此由单风险隶属度确定多风险隶属度实现的是模糊状态转换,所以支撑隶属度转换的不是二值逻辑而是多值逻辑.在多值逻辑研究中,基于"取大取小"推理的模糊逻辑不是数学逻辑,"加权平均"的模糊综合评判是"假设"不是推理.所以处理模糊信息的多值逻辑尚需深入研究.指出隶属度转换不是线性转换的原因是,单风险模糊隶属度中可能包含对确定多风险隶属度不起作用的非线性冗余值.通过确定冗余值的数学表达式建立冗余理论,用冗余理论界定模糊隶属度转换不是线性转换,并推导去冗算法实现隶属度转换.由此建立处理模糊信息的多值逻辑.     

基于随机采样模糊聚类和卡尔曼滤波方法的模糊辨识

提出一种新的基于模糊聚类和卡尔曼滤波方法的模糊辨识算法 .该方法是基于快速模糊聚类 ,计算给定样本在各类中的隶属度 ,并利用卡尔曼滤波方法辨识模糊模型的结论参数 .整个辨识过程与一般的模糊聚类方法 [1 ]相比 ,需要的 CPU时间大大缩短 .最后通过仿真实例验证了该方法的有效性 .     

基于非线性冗余算法的凝汽器状态评价

凝汽器状态评价指标是具有不同量纲的特征参数,为避免对具有不同量纲的特征空间实施无量纲化变换造成的信息失真,隶属度转换模式成为凝汽器状态评价的基本模式.由于特征参数状态渐变连续,所以表征参数状态的指标隶属度是模糊隶属度.模糊隶属度表征模糊状态,模糊隶属度转换实现模糊状态转换.用冗余理论界定模糊隶属度转换的非线性,用非线性去冗算法实现模糊隶属度转换并建立凝汽器状态评价的非线性评价模型.     

毕达哥拉斯模糊软集的相似性测度及其应用

直觉模糊软集不能处理隶属度与非隶属度之和大于1的情况,且现有的直觉模糊软集的相似性测度只考虑了隶属度与非隶属度,忽视了犹豫度。针对以上问题,本文提出了一种基于隶属度、非隶属度以及犹豫度三个参数的毕达哥拉斯模糊软集的相似性测度和加权相似性测度。在为加权相似性测度的权重取值时,本文基于现有文献中直觉模糊熵存在的缺陷建立一种改进的直觉模糊熵,利用熵权法计算权重。分别讨论两相似性测度公式的性质,最后将两相似性侧度公式应用在建筑材料的模式识别问题中。     

基于交叉影响的IFWGA算子及其在多属性决策中的应用

不同直觉模糊数在信息集结过程中,其隶属度与非隶属度之间可能存在着相互影响.提出了直觉模糊数上的改进的乘法运算和幂运算,重新给出了直觉模糊加权几何平均算子和直觉模糊有序加权几何平均算子的表达式,并研究了他们的一些性质.最后通过实例说明了新的IFWGA集成算子在多属性决策中的应用是可行和有效的.     

梯形直觉模糊数排序方法及在多属性决策中应用

基于梯形直觉模糊数的值和模糊度两个特征,一类梯形直觉模糊数的排序方法被研究.首先,给出了梯形直觉模糊数的定义、运算法则和截集.其次,定义了梯形直觉模糊数关于隶属度和非隶属度的值和模糊度,以及值的指标和模糊度的指标.最后,给出了梯形直觉模糊数的排序方法,并将其应用到属性值为梯形直觉模糊数的多属性决策问题中.     

带模糊参数的系统模糊可靠度分析

含模糊参数系统的可靠性理论研究具有广泛的实际应用背景,但由于模糊数运算的隶属函数表达困难,影响和制约着模糊参数系统的模糊可靠性理论与应用的研究。本文利用模糊数的结构元表示,给出了模糊表达式隶属函数确定的两种方法,进而得到了具有模糊参数的不可修复串联和并联系统模糊可靠度的隶属函数表达式。     

典型模糊控制器的隶属函数设计及分析

基于一类sum-product模糊推理算子、重心解模糊化法和单点输出的典型模糊控制器的解析结构推导,深入分析了输入隶属函数对模糊控制系统性能的影响,提出了隶属函数的系统化设计方法.并在此基础上,设计了一种具有等差间距因子的三角形隶属函数并推导了其模糊控制器的解析结构,仿真结果证明了该设计的有效性.     

小波分析与模糊系统分析的联系

讨论小波基函数与模糊集隶属函数之间的联系 ,证明模糊系统研究与应用中的隶属函数都可以由称之为简单小波的母函数表出 ,反之每一简单小波也可以由某一隶属函数表出 ,从而验证简单小波类与某一隶属函数类的一一对应关系。该结果进一步揭示模糊推理和模糊控制的实质 ,为在模糊系统辨识、模糊控制和模糊数据分析等诸多领域充分利用近代小波分析的成果提供新思路     

Analysis of maximum total return in the continuous knapsack problem with fuzzy object weights

This paper proposes a parametric programming approach to analyze the fuzzy maximum total return in the continuous knapsack problem with fuzzy objective weights, in that the membership function of the maximum total return is constructed. The idea is based on Zadeh’s extension principle, α-cut representation, and the duality theorem of linear programming. A pair of linear programs parameterized by possibility level α is formulated to calculate the lower and upper bounds of the fuzzy maximum total return at α, through which the membership function of the maximum total return is constructed. To demonstrate the validity of the proposed procedure, an example studied by the previous studies is investigated successfully. Since the fuzzy maximum total return is completely expressed by a membership function rather than by a crisp value reported in previous studies, the fuzziness of object weights is conserved completely, and more information is provided for making decisions in real-world resource allocation applications. The generalization of the proposed approach for other types of knapsack problems is also straightforward.     

基于相对隶属度的织物风格模糊聚类分析

分析比较了统计数学、模糊数学和灰色数学在织物风格分析中的缺陷.特别针对模糊数学需人为确定隶属函数的缺陷提出采用基于相对隶属度的织物风格模糊聚类分析方法.以相对隶属度矩阵和模糊聚类中心矩阵的迭代求解为依据,对苎麻等十种织物的风格测试参数进行了聚类分析.通过聚类计算得到了织物相对类别特征值的聚类结果.经验证认为该结果符合客观实际和人们的感知经验.     

Evaluation of fuzzy linear regression models by comparing membership functions

Fuzzy linear regression models can provide an estimated fuzzy number that has a fuzzy membership function. If a point that has the highest membership value from the estimated fuzzy number is not within the support of the observed fuzzy membership function, a decision-maker can have high risk from the estimate. In this study a modification of fuzzy linear regression analysis based on a criterion of minimizing the difference of the fuzzy membership values between the observed and estimated fuzzy numbers is proposed. Two numerical examples are used to evaluate the fuzzy regression models.     

Analysis of critical paths in a project network with fuzzy activity times

This paper proposes an approach to critical path analysis for a project network with activity times being fuzzy numbers, in that the membership function of the fuzzy total duration time is constructed. The basic idea is based on the extension principle and linear programming formulation. A pair of linear programs parameterized by possibility level α is formulated to calculate the lower and upper bounds of the fuzzy total duration time at α. By enumerating different values of α, the membership function of the fuzzy total duration time is constructed, and the fuzzy critical paths are identified at the same time. Moreover, by applying the Yager ranking method, definitions of the most critical path and the relative degree of criticality of paths are developed; and these definitions are theoretically sound and easy to use in practice. Two examples with activity times being fuzzy numbers of L-R and L-L types discussed in previous studies are solved successfully to demonstrate the validity of the proposed approach. Since the total duration time is completely expressed by a membership function rather than by a crisp value, the fuzziness of activity times is conserved completely, and more information is provided for critical path analysis.     

Application of Fuzzy Linear Programming in Production Planning

In this paper, a new fuzzy linear programming based methodology using a specific membership function, named as modified logistic membership function is proposed. The modified logistic membership function is first formulated and its flexibility in taking up vagueness in parameters is established by an analytical approach. This membership function is tested for its useful performance through an illustrative example by employing fuzzy linear programming. The developed methodology of FLP has provided a confidence in applying to real life industrial production planning problem. This approach of solving industrial production planning problem can have feed back within the decision maker, the implementer and the analyst. In such case this approach can be called as IFLP (Interactive Fuzzy Linear Programming). There is a possibility to design the self organizing of fuzzy system for the mix products selection problem in order to find the satisfactory solution. The decision maker, the analyst and the implementer can incorporate their knowledge and experience to obtain the best outcome.     

D.C.隶属函数模糊集及其应用(I)--D.C.隶属函数模糊集基本概念与性质

本文是D.C.隶属函数模糊集及其应用系列研究的第一部分.建立了D.C.隶属函数模糊集的基本概念.探讨了D.C.隶属函数模糊集的基本性质和D.C.隶属函数模糊集对一些常见的重要t模、余模和伪补的封闭性.并以此建立了丰富的模糊数学应用模型.     

Interval optimization based line sampling method for fuzzy and random reliability analysis

For structural system with fuzzy variables as well as random variables, a novel algorithm for obtaining membership function of fuzzy reliability is presented on interval optimization based Line Sampling (LS) method. In the presented algorithm, the value domain of the fuzzy variables under the given membership level is firstly obtained according to their membership functions. Then, in the value domain of the fuzzy variables, bounds of reliability of the structure are obtained by the nesting analysis of the interval optimization, which is performed by modern heuristic methods, and reliability analysis, which is achieved by the LS method in the reduced space of the random variables. In this way the uncertainties of the input variables are propagated to the safety measurement of the structure, and the membership function of the fuzzy reliability is obtained. The presented algorithm not only inherits the advantage of the direct Monte Carlo method in propagating and distinguishing the fuzzy and random uncertainties, but also can improve the computational efficiency tremendously in case of acceptable precision. Several examples are used to illustrate the advantages of the presented algorithm.