模糊关系方程的解的模糊遗传导优算法

在一般遗传算法ＧＡ的基础上，基于模糊集理论中的模糊关系方程的解的寻优问题提出了模糊遗传算法ＦＧＡ，它能有效地找出模糊关系方程的解的寻优问题的近似最优解。美学 给出了一个重要的定理：模糊模式定理。     

一种模糊随机动态有限元方程的解法

本文对模糊随机动态有限元方程的解法做了较系统的探讨，指出当对模糊随机动态有限元方程做λ－截集时，将得到一个区间随机动态有限元平衡方程。在满足一定的条件下，利用区间数和模糊分解定理，该方程将存在唯一最大和最小的两个解，即行到一个解的区间。最后，算例表明其计算量将是计算普通随机有限元方程的两倍以上。     

燃气危险性模糊评价的逆问题

模糊评价已在燃气危险性评价中得到应用，但其中的权重取值主观性较大，本文利用已得到的评价信息，通过解模糊关系方程得到模糊评价问题逆解，针对某燃气站瓶库单元的分析，得到单元各因素的权重分配范围。     

一种采用自适应遗传算法的模糊自整定控制器

针对模糊自整定控制器参数寻优能力差的不足,研究了采用自适应交叉概率与变异概率的遗传算法,提出了用这种自适应遗传算法改善模糊自整定控制器性能的方法。对采用自适应遗传算法的模糊自整定控制器与一般的模糊自适应控制器作了仿真对比研究,说明了前者的优越性。     

不完备模糊信息系统

以不完备模糊信息系统为研究对象，建立了其中的模糊相容关系及模糊粗糙上、下近似集。在此基础上，探讨了论域上的模糊覆盖问题并提出了覆盖的3种运算形式；定义了2种新的模糊粗糙熵以讨论不完备模糊信息系统中的不确定性因素，证明了不确定因素的变化与度量强度之间的重要关系；建立了一种度量部分模糊知识依赖的新方法，获得了一些新的定理结果证明。     

半线性时滞模糊微分方程解的存在性

模糊微分方程常用来模拟不确定条件下的变化过程．其理论被广泛的应用在很多不同的实际问题上．本文主要研究半线性时滞模糊微分方程解的存在性．在广义Hukuhara导数的框架下,运用了一个弱压缩映射的不动点定理,将半线性时滞模糊微分方程解的存在性问题转化为算子不动点的存在性问题,从而建立了方程解的存在性定理．文中例子说明了该理论结果的实用性．     

一种混合优化策略在工步排序中的应用

结合创成式ＣＡＰＰ系统中工步优化问题，介绍了由遗传算法（ＧｅｎｅｔｉｃＡｌｇｏｒｉｔｈｍｓ，ＧＡ）和模拟退火算法（ＳｉｍｕｌａｔｅｄＡｎｎｅａｌｉｎｇＡｌｇｏｒｉｔｈｍｓ，ＳＡ）构成的混合寻优策略。最后以一箱体的工艺规划过程为例，将基于混合寻优策略的工步排序融入以加工中心为主要加工设备的ＣＡＰＰ工艺决策过程。     

球壳的模糊弹粘塑性分析

本文基于文［１］提出和建立的模糊弹粘塑性力学本构方程，给出了球壳问题的解，并进行了分析计算。所得结果是令人满意的。     

带有积压定单的最优模糊存储模型

本文提出一个新的具有积压定单的关于模糊总需求的模糊存储模型。在模糊函数原理下，给出了模糊总存储成本。为了寻找最优解，使用积分均值法白化模糊总存储成本，利用Lingo8．0求解不等式约束问题，我们发现最优解都是确定的实数。此外，当模糊总需求是确定的实数时，我们提出模型的最优解与经典的具有积压定单存储模型具有相同的结果。     

一种改进的模糊神经网络控制器学习算法

提出了一种改进的模糊神经网络混合学习算法,运用遗传算法优化构成隶属函数的网络结构,运用最小二乘法进行解模糊,具有更高的学习精度和更快的收敛速度,解决了在多变量系统中采用模糊神经网络时学习收敛慢且易陷入局部极小点的问题。     

Fuzzy Based Hybrid Focus Value Estimation for Multi Focus Image Fusion

Due to limited depth-of-field of digital single-lens reflex cameras, the scene content within a limited distance from the imaging plane remains in focus while other objects closer to or further away from the point of focus appear as blurred (out-of-focus) in the image. Multi-Focus Image Fusion can be used to reconstruct a fully focused image from two or more partially focused images of the same scene. In this paper, a new Fuzzy Based Hybrid Focus Measure (FBHFM) for multi-focus image fusion has been proposed. Optimal block size is very critical step for multi-focus image fusion. Particle Swarm Optimization (PSO) algorithm has been used to find optimal size of the block of the images for extraction of focus measure features. After finding optimal blocks, three focus measures Sum of Modified Laplacian, Gray Level Variance and Contrast Visibility has been extracted and combined these focus measures by using intelligent fuzzy technique. Fuzzy based hybrid intelligent focus values were estimated using contrast visibility measure to generate focused image. Different sets of multi-focus images have been used in detailed experimentation and compared the results with state-of-the-art existing techniques such as Genetic Algorithm (GA), Principal Component Analysis (PCA), Laplacian Pyramid discrete wavelet transform (DWT), and aDWT for image fusion. It has been found that proposed method performs well as compare to existing methods.

     

Dynamic Response Analysis of Fuzzy Stochastic Truss Structures under Fuzzy Stochastic Excitation

A novel method (Fuzzy factor method) is presented, which is used in the dynamic response analysis of fuzzy stochastic truss structures under fuzzy stochastic step loads. Considering the fuzzy randomness of structural physical parameters, geometric dimensions and the amplitudes of step loads simultaneously, fuzzy stochastic dynamic response of the truss structures is developed using the mode superposition method and fuzzy factor method. The fuzzy numerical characteristics of dynamic response are then obtained by using the random variable’s moment method and the algebra synthesis method. The influences of the fuzzy randomness of structural physical parameters, geometric dimensions and step load on the fuzzy randomness of the dynamic response are demonstrated via an engineering example, and Monte-Carlo method is used to simulate this example, verifying the feasibility and validity of the modeling and method given in this paper.     

一种改进型自适应加权模糊均值滤波算法

针对原有的自适应加权模糊均值AWFM滤波器对局部噪声幅度估计不足的缺点。提出了一种新的改进型自适应加权模糊均值MAWFM滤波算法。该算法采用了一种新的模糊检测方法，可以同时检测各个像素点的正负向噪声幅度，从而能够建立一种新的自适应的模糊信号空间，以适应噪声在各个图像局部的变化。实验结果表明，MAWFM滤波器比以前的AWFM滤波器及传统的中值滤波器均有较优越的性能。     

Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs. The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. There are several types of input MFs which can be introduced in FIS, commonly chosen based on the type of real data, sensitivity of certain rule implied and computational limits. This paper focuses on the construction of interval type 2 (IT2) trapezoidal shape MF from fuzzy C Means (FCM) that is used for fuzzification process of mamdani FIS. In the process, upper MF (UMF) and lower MF (LMF) of the MF need to be identified to get the range of the footprint of uncertainty (FOU). This paper proposes Genetic tuning process, which is a part of genetic algorithm (GA), to adjust parameters in order to improve the behavior of existing system, especially to enhance the accuracy of the system model. This novel process is a hybrid approach which produces Genetic Fuzzy System (GFS) that helps to enhance fuzzy classification problems and performance. The approach provides a new method for the construction and tuning process of the IT2 MF, based on the FCM outcomes. The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA method. It is shown that the proposed approach is able to outperform the mentioned benchmarked approaches. The work implies a wider range of IT2 MF types, constructed based on FCM outcomes, and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction, analytics and rule-based solutions.     

模糊关系数据库的研究与实现

阐述了一个模糊关系数据库系统的实验性原型ＦＯＤＢ１以及基于该系统的模糊关系数据理论。首先，对模糊关系数据库领域进行介绍；然后，阐述ＦＲＤＢＩ系统及其中的模糊关系和模糊查询语言ＦＳＱＬ；最后，讨论了模糊关系数据理论。     

Cause and effect analysis by fuzzy relational equations and a genetic algorithm

This paper proposes using a genetic algorithm as a tool to solve the fault diagnosis problem. The fault diagnosis problem is based on a cause and effect analysis which is formally described by fuzzy relations. Fuzzy relations are formed on the basis of expert assessments. Application of expert fuzzy relations to restore and identify the causes through the observed effects requires the solution to a system of fuzzy relational equations. In this study this search for a solution amounts to solving a corresponding optimization problem. An optimization algorithm is based on the application of genetic operations of crossover, mutation and selection. The genetic algorithm suggested here represents an application in expert systems of fault diagnosis and quality control.     

风险决策模糊数学模型在企业决策中的应用

风险型决策问题是决策科学中的一个重要问题,本文针对其模糊不确定性,建立了自然状态概率的模糊估算模型,然后,基于决策论中的最优期望益损值决策准则进行最优决策,并通过实际应用说明该模型的有效性和可靠性,本文的研究为风险型决策问题的解决提供了一种新的定量分析方法,因此,具有重要的理论与应用价值。     

Information Theoretic Weighted Fuzzy Clustering Ensemble

In order to improve performance and robustness of clustering, it is proposed to generate and aggregate a number of primary clusters via clustering ensemble technique. Fuzzy clustering ensemble approaches attempt to improve the performance of fuzzy clustering tasks. However, in these approaches, cluster (or clustering) reliability has not paid much attention to. Ignoring cluster (or clustering) reliability makes these approaches weak in dealing with low-quality base clustering methods. In this paper, we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted co-association matrix Fuzzy C-Means (RBFCM), Reliability Based Graph Partitioning (RBGP) and Reliability Based Hyper Clustering (RBHC) as three new fuzzy clustering consensus functions. Our fuzzy clustering ensemble approach works based on fuzzy cluster unreliability estimation. Cluster unreliability is estimated according to an entropic criterion using the cluster labels in the entire ensemble. To do so, the new metric is defined to estimate the fuzzy cluster unreliability; then, the reliability value of any cluster is determined using a Reliability Driven Cluster Indicator (RDCI). The time complexities of RBHC and RBGP are linearly proportional with the number of data objects. Performance and robustness of the proposed method are experimentally evaluated for some benchmark datasets. The experimental results demonstrate efficiency and suitability of the proposed method.     

基于模糊数学的故障诊断专家系统的设计和实现

分析了故障诊断中故障现象与原因之间存在的模糊关系，引入模糊数学理论，利用VisualC 程序语言设计和实现了模糊诊断专家系统。     

应用遗传算法和神经网络的工程电梯传动机构模糊优化设计

 考虑到影响设计的某些因素很难用确定数值表示,以工程电梯传动机构中蜗轮齿冠体积最小为优化目标,建立了模糊优化设计的数学模型。采用二级模糊综合评判法按最大隶属度原则求出最优水平截集,将模糊优化问题转化为普通优化问题。另外,通过神经网络方法得出网络权值和阈值以拟合待求系数,并采用加法形式的惩罚策略来构造带有惩罚项的适值函数,应用Matlab遗传算法工具箱寻求问题最优解,从而提高设计精度和搜索效率。