基于SSCL的模糊C均值图像分类方法

针对传统模糊C均值聚类方法对噪声敏感和过分依赖于初始聚类中心的缺点,提出基于SSCL的模糊C均值图像分类的自适应算法。该算法首先通过SSCL获得初始类别数和类别中心,然后作为模糊C均值聚类的输入,自动对图像进行分割,并对图像分割结果利用空间信息进行后处理。实验结果表明该方法较好地解决了FCM算法中的初始化和噪声敏感问题,具有较好的分类结果。     

Learning fuzzy rules from fuzzy samples based on rough set technique

Although the traditional rough set theory has been a powerful mathematical tool for modeling incompleteness and vagueness, its performance in dealing with initial fuzzy data is usually poor. This paper makes an attempt to improve its performance by extending the traditional rough set approach to the fuzzy environment. The extension is twofold. One is knowledge representation and the other is knowledge reduction. First, we provide new definitions of fuzzy lower and upper approximations by considering the similarity between the two objects. Second, we extend a number of underlying concepts of knowledge reduction (such as the reduct and core) to the fuzzy environment and use these extensions to propose a heuristic algorithm to learn fuzzy rules from initial fuzzy data. Finally, we provide some numerical experiments to demonstrate the feasibility of the proposed algorithm. One of the main contributions of this paper is that the fundamental relationship between the reducts and core of rough sets is still pertinent after the proposed extension.     

Approaching the ocean color problem using fuzzy rules

In this paper, we propose a fuzzy logic-based approach which exploits remotely sensed multispectral measurements of the reflected sunlight to estimate the concentration of optically active constituents of the sea water. The relation between the concentrations of interest and the subsurface reflectances is modeled by a set of fuzzy rules extracted automatically from the data through a two-step procedure. First, a compact initial rule base is generated by projecting onto the input variables the clusters produced by a fuzzy clustering algorithm. Then, a genetic algorithm is applied to optimize the rules. Appropriate constraints maintain the semantic properties of the initial model during the genetic evolution. Results of the application of the fuzzy model obtained from data simulated with an ocean color model over the channels of the Medium Resolution Imaging Spectrometer are shown and discussed.     

模糊C均值聚类图像分割的改进遗传算法研究

基于模糊C均值（FCM）聚类算法,并利用遗传算法全局随机搜索的特点,提出了一种图像分割的改进遗传算法。该算法首先采用一种初值化算法确定合适的遗传算法的初始搜索范围,然后对遗传算法中的编码方式、交叉算子、变异算子等参数进行了一些适当改进,进而给出了该算法的理论推导和算法的具体实现步骤。该算法除了解决模糊C均值聚类算法在医学图像分割中容易陷入局部最优解的问题,而且采用的初值化算法比标准的遗传模糊C均值聚类算法能确定更合适的遗传算法的初始搜索范围,从而加速了遗传算法的收敛过程。实验表明,该方法相对于标准的遗传模糊C均值聚类算法,效果要好得多。     

基于聚类和遗传算法的解释性模糊模型设计

提出了一种基于模糊聚类和遗传算法构建解释性模糊模型的设计方法。定义了模糊模型的精确性指标,给出了模糊模型解释性的必要条件。然后利用模糊聚类算法和最小二乘法辨识初始的模糊模型;采用多目标遗传算法优化模糊模型;为提高模型的解释性,在遗传算法中利用基于相似性的模糊集合和模糊规则的简化方法对模型进行约简。采用该方法对Mackey-Glass系统进行建模,仿真结果验证了该方法的有效性。     

基于改进人工蜂群的模糊C-均值聚类算法

模糊C-均值聚类算法在数据挖掘领域有着广泛的使用背景,而对初始点的敏感和较差的搜索能力,限制了算法的进一步推广应用。人工蜂群算法具有对初始点不敏感、适应能力强和搜索能力强等优点,并且针对人工蜂群算法对单峰问题收敛速度慢、多峰问题容易陷入局部最优等问题,通过引入差分进化算法中变异和交叉思想,改善蜂群算法的收敛速度,平衡局部搜索和全局搜索能力。然后将改进的人工蜂群算法和模糊C-均值聚类算法结合得到基于改进人工蜂群的模糊C-均值聚类算法,并在多个国际标准数据集上进行验证,实验结果表明此算法在多个衡量指标上取得了明显的改进。     

基于模糊超球的模糊神经网络初始参数设定

在人们常用到的模糊神经学习算法中很少讨论如何设置并调整初始的权值参数,这样就会影响模糊神经学习算法的准确度,使得目标函数值很大。虽然在改进的模糊神经学习算法中使用模糊C均值聚类方法来确定模糊规则的初始参数,但是这种方法必须已知模式集的数目,这就限制了模糊神经网络的应用范围。因此,将模糊超球神经网络的思想与模糊神经网络有机的结合起来,通过不断调整超球的中心和半径以及超球的数目,优化模式集的边界,来确定权值参数,利用这种方法确定初始参数可以减小误差,提高算法的准确度,使目标函数值减小。     

Genetic tabu search for the fuzzy flexible job shop problem

This paper tackles the flexible job-shop scheduling problem with uncertain processing times. The uncertainty in processing times is represented by means of fuzzy numbers, hence the name fuzzy flexible job-shop scheduling. We propose an effective genetic algorithm hybridised with tabu search and heuristic seeding to minimise the total time needed to complete all jobs, known as makespan. To build a high-quality and diverse set of initial solutions we introduce a heuristic method which benefits from the flexible nature of the problem. This initial population will be the starting point for the genetic algorithm, which then applies tabu search to every generated chromosome. The tabu search algorithm relies on a neighbourhood structure that is proposed and analysed in this paper; in particular, some interesting properties are proved, such as feasibility and connectivity. Additionally, we incorporate a filtering mechanism to reduce the neighbourhood size and a method that allows to speed-up the evaluation of new chromosomes. To assess the performance of the resulting method and compare it with the state-of-the-art, we present an extensive computational study on a benchmark with 205 instances, considering both deterministic and fuzzy instances to enhance the significance of the study. The results of these experiments clearly show that not only does the hybrid algorithm benefit from the synergy among its components but it is also quite competitive with the state-of-the-art when solving both crisp and fuzzy instances, providing new best-known solutions for a number of these test instances.     

基于模糊神经网络的飞机某系统故障诊断研究

提出一种基于模糊神经网络的飞机某系统故障诊断方法。利用改进的模糊C均-值聚类算法进行结构辨识,从而自动获得模糊规则库,并得到模糊模型的初始参数;然后生成与之相匹配的初始模糊神经网络,并通过学习算法训练网络来进行参数辨识,得到一个精确的模糊模型。将该系统地面实测数据作为样本数据,建立起了基于模糊神经网络的飞机某系统故障诊断模型。最后对该模型进行测试与分析,结果表明该方法具有抗噪、抗敏感、诊断准确度高等优点。     

基于模糊超球的模糊神经网络初始参数设定

在人们常用到的模糊神经学习算法中很少讨论如何设置并调整初始的权值参数，这样就会影响模糊神经学习算法的准确度。使得目标函数值很大。虽然在改进的模糊神经学习算法中使用模糊C均值聚类方法来确定模糊规则的初始参数。但是这种方法必须已知模式集的数目，这就限制了模糊神经网络的应用范围。因此，将模糊超球神经网络的思想与模糊神经网络有机的结合起来，通过不断调整超球的中心和半径以及超球的数目，优化模式集的边界，来确定权值参数，利用这种方法确定初始参数可以减小误差，提高算法的准确度，使目标函数值减小。     

动态的模糊K-Modes初始化算法

模糊-Modes聚类算法针对分类属性的数据进行聚类,使用爬山法来寻找最优解,因此该算法对初始值较为敏感。为了克服该缺点,提出一种动态的模糊K—Modes初始化算法,该方法能够自动确定聚类数目,以及对应的聚类中心;而且能够应用于数值属性和分类属性相混合的数据集。该初始化算法可以有效地克服模糊K—Modes算法对初值的敏感性。实验的结果表明了该初始化算法的可行性和有效性。     

基于多目标遗传算法的TS模糊模型设计

提出一种利用遗传算法进行TS模糊模型的优化设计方法。首先定义了TS模糊模型的精确性指标,给出模糊模型解释性的必要条件。然后利用模糊聚类算法和最小二乘法辨识初始的模糊模型;利用多目标遗传算法优化模糊模型;为提高模型的解释性,在遗传算法中利用基于相似性的模糊集合和模糊规则简化方法对模型进行约简。最后利用该方法进行一类二阶合成非线性动态系统的建模,仿真结果验证了该方法的有效性。     

A genetic approach to modeling fuzzy systems based on information granulation and successive generation-based evolution method

In this paper, we introduce and investigate a new category of fuzzy inference systems based on information granulation and genetic optimization used to system identification. We show the applications of such systems to identification of nonlinear systems. The formal framework of information granulation and resulting information granules themselves become an important design facet of the fuzzy models. By embracing fuzzy sets, the model is geared towards capturing essential relationship between information granules rather than concentrating on plain numeric data. Information granulation realized with the use of the commonly exploited C-Means clustering helps determine the initial values of the parameters of the fuzzy models. This in particular concerns such essential components of the rules as the initial apexes of the membership functions standing in the premise part of the fuzzy rules and the points of the polynomial functions standing in the consequence part. The initial apexes (center points) of the membership functions based on C-Means algorithm are tuned with the aid of the genetic algorithm (GA), while the tuned apexes are also used to adjust the points of the consequent polynomials (conclusions) of the rules. In particular, the initial apexes of the membership functions and the initial points of the consequent polynomials are adjusted and updated every time through successive evolution process. The overall design methodology involves a hybrid structural and parametric optimization. Genetic algorithms and C-Means clustering are used to optimize the model with respect to its structure and parameters. To determine the structure and estimate the values of the parameters of the fuzzy model we consider the successive tuning method with generation-based evolution by means of genetic algorithms. The model is evaluated with the use of numerical experimentation and its quality is compared with respect to some other fuzzy models already encountered in the literature.     

基于改进的FCM的人脑MR图像分割

传统模糊C均值广泛应用于图像分割,它是一种经典的模棚聚类分析方法,但是FCM算法对于初始值的选择都是采取随机的方法,强烈依赖于初始值的选择,收敛结果容易陷入局部最小值,并且FCM并没有考虑图像的空间信息,因而对噪声十分敏感。提出改进的FCM方法,采用新的方法确定初始值的选择,然后考虑空间信息,利用Gibbs随机场的性质引入先验邻域约束信息,重新确定像素的模糊隶属度值,同时再进一步地调整距离矩阵。通过实验可以表明,此改进的方法具有很好的分割效果,同时对噪声具有较强的鲁棒性。     

一种基于大密度区域的模糊聚类算法

针对模糊C-均值(FCM)算法对初始聚类中心和噪声数据敏感的缺陷,提出一种基于大密度区域的模糊聚类算法.该算法首先利用大密度区域以及样本的密度值变化方法,选取初始聚类中心以及候选初始聚类中心,并依据初始聚类中心与候选初始聚类中心的距离,确定初始聚类中心点,从而有效的克服了随机给定初始聚类中心容易使算法收敛到局部极小的缺陷;其次,分别利用密度函数为样本加权和引用改进的隶属度函数进行优化,有效地提高了模糊聚类的抗噪性;最后实验验证了算法在初始聚类中心的确定,聚类效果和抗噪性方面具有良好的效果.     

基于局部线性聚类算法的模糊建模

提出了一种新的基于T-S模糊模型的建模方法，首先通过一种局部线性聚类算法，自适应确定模糊规则数目及初始T-S模型的前提和结论参数，建立相应的一阶T-S模糊神经网络．并用梯度下降和递推最小二乘混合算法训练网络参数，从而提高建模精度．最后，通过两个仿真实例验证了本文方法的有效性．     

Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters

In this paper, we present an agglomerative fuzzy $k$-means clustering algorithm for numerical data, an extension to the standard fuzzy $k$-means algorithm by introducing a penalty term to the objective function to make the clustering process not sensitive to the initial cluster centers. The new algorithm can produce more consistent clustering results from different sets of initial clusters centers. Combined with cluster validation techniques, the new algorithm can determine the number of clusters in a data set, which is a well known problem in $k$-means clustering. Experimental results on synthetic data sets (2 to 5 dimensions, 500 to 5000 objects and 3 to 7 clusters), the BIRCH two-dimensional data set of 20000 objects and 100 clusters, and the WINE data set of 178 objects, 17 dimensions and 3 clusters from UCI, have demonstrated the effectiveness of the new algorithm in producing consistent clustering results and determining the correct number of clusters in different data sets, some with overlapping inherent clusters.     

一种基于蚁群算法的模糊C均值聚类

针对模糊C均值(FCM)聚类算法,在选取聚类中心点时采用随机选取易使得迭代过程陷入局部最优解,FCM算法自身并不能确定聚类个数需要人为设定,并在数据分类应用时具有了一定误差的问题,提出了一种基于蚁群算法的FCM聚类算法。该算法根据蚁群聚类算法确定模糊聚类个数和FCM算法的初始聚类中心：利用蚁群算法的全局搜索性、并行计算性等特点避免了聚类陷入局部最优解：仿真结果表明了该算法的有效性。     

基于模糊c-means算法的空间数据分类和预测

空间分类和预测是空间数据挖掘中一个非常重要的方法,但对它们的研究目前尚处于初始阶段.通过引入空间对象对模糊聚类的模糊隶属度的概念,提出了基于模糊c-means算法的空间数据分类和预测的方法(SFCM),该方法首先用模糊c-means方法对数据集论域空间进行聚类,但由于空间数据具有空间自相关的特性,在用模糊c-means算法进行空间聚类时加入了空间信息,然后计算每个空间对象对所有聚类的模糊隶属度并从中找出模糊隶属度最大的聚类,最后用该聚类中心对象的因变量的值作为该空间对象的因变量的估计值,理论分析和实验结果表明,该算法是有效可行的.     

Hybrid identification of fuzzy rule‐based models

In this study, we propose a hybrid identification algorithm for a class of fuzzy rule‐based systems. The rule‐based fuzzy modeling concerns structure optimization and parameter identification using the fuzzy inference methods and hybrid structure combined with two methods of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model concern a simplified and linear type of inference. The proposed hybrid optimal identification algorithm is carried out using a combination of genetic algorithms and an improved complex method. The genetic algorithms determine initial parameters of the membership function of the premise part of the fuzzy rules. In the sequel, the improved complex method (being in essence a powerful auto‐tuning algorithm) leads to fine‐tuning of the parameters of the respective membership functions. An aggregate performance index with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model obtained for the training and testing data. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature. © 2002 John Wiley & Sons, Inc.