基于改进型模糊聚类的模糊系统建模方法

结合减法聚类和模糊C均值聚类,提出了一种改进型聚类算法,加快了收敛速度．利用改进后的算法对模糊系统输入或输出的样本集聚类,对聚类结果采用Trust-Region法拟合高斯型和S型函数,以实现模糊系统输入、输出空间的划分和隶属度函数参数的确定．结合MATLAB的模糊和曲线拟合工具箱,详述了如何在标准算法上进行改进和模糊系统建模．通过对IRIS标准数据聚类实验以及在解决机械加工误差复映问题上的应用,验证了改进后算法和建模方法的有效性．     

基于FCM和遗传算法的图像模糊聚类分析

聚类分析在模式识别和图像处理领域中有着极为重要的意义和广泛的应用前景。常用的聚类分析的方法是模糊C均值算法（FCM）,但是FCM算法容易陷入局部最优解。提出一种基于FCM和遗传算法对图像进行模糊聚类分析的方法。对输入图像进行纹理特征提取,通过主成分分析法对提取的特征向量进行降维处理,降低图像聚类分析算法的复杂度,提高结果的精确度,结合FCM和遗传算法对图像数据进行模糊聚类分析。实验结果表明该方法可以得到较好的分类效果。     

A novel hybrid method based on fuzzy cognitive maps and fuzzy clustering algorithms for grading celiac disease

This paper presents a new method based on fuzzy cognitive map (FCM) and possibilistic fuzzy c-means (PFCM) clustering algorithm for categorizing celiac disease (CD). CD is a complex disorder whose development is affected by genetics (HLA alleles) and gluten ingestion. The celiac patients who are not treated are at a high risk of cancer, malignant lymphoma, and small bowel neoplasia. Therefore, CD diagnosis and grading are of paramount importance. The proposed FCM models human thinking for the purpose of classifying patients suffering from CD. We used the latest grading method where three grades A, B1, and B2 are used. To improve FCM efficiency and classification capability, a nonlinear Hebbian learning algorithm is applied for adjusting the FCM weights. To this end, 89 cases are studied. Three experts extracted seven main determinant characteristics of CD which were considered as FCM concepts. The mutual effects of these concepts on one another and on the final concept were expressed in the form of fuzzy rules and linguistic variables. Using the center of gravity defuzzifier, we obtained the numerical values of these weights and obtained the total weight matrix. Ultimately, combining the FCM model with PFCM algorithm, we obtained the grades A, B1, and B2 accuracies as 88, 90, and 91%, respectively. The main advantage of the proposed FCM is the good transparency and interpretability in the decision-making procedure, which make it a suitable tool for daily usage in the clinical practice.

     

A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques

In this paper, we extend the work of Kraft et al. to present a new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques. First, we present a fuzzy agglomerative hierarchical clustering algorithm for clustering documents and to get the document cluster centers of document clusters. Then, we present a method to construct fuzzy logic rules based on the document clusters and their document cluster centers. Finally, we apply the constructed fuzzy logic rules to modify the user's query for query expansion and to guide the information retrieval system to retrieve documents relevant to the user's request. The fuzzy logic rules can represent three kinds of fuzzy relationships (i.e., fuzzy positive association relationship, fuzzy specialization relationship and fuzzy generalization relationship) between index terms. The proposed fuzzy information retrieval method is more flexible and more intelligent than the existing methods due to the fact that it can expand users' queries for fuzzy information retrieval in a more effective manner.     

基于加权快速聚类的异常数据挖掘算法

聚类是数据挖掘领域中最活跃的研究分支之一,并在其他的科学领域也有广泛的应用。设计了基于加权快速聚类的异常数据挖掘算法,以便能快速发现异常数据。首先通过对数据的每个属性赋予一定权值,权值的大小要体现其对分类的贡献度,并根据属性权值的特点,选择比较优良的初始分区,然后进行多次迭代,得到接近最优分区,接着运用一定规则,发现异常数据类,最后实践证明该技术取得很好的社会效果。     

软硬结合的快速模糊C-均值聚类算法的研究

讨论的是对模糊C-均值聚类方法的改进,在原有的模糊C-均值算法的基础上,提出一种软硬结合的快速模糊C-均值聚类算法。快速模糊C-均值聚类算法是在模糊C-均值聚类算法之前加入一层硬C-均值聚类算法。硬聚类算法能比模糊聚类算法以高得多的速度完成,将硬聚类中心作为模糊聚类中心的迭代初值,从而提高模糊C-均值聚类算法的收敛速度,这对于大量数据的聚类是很有意义的。用数据仿真验证了这种快速模糊C-均值聚类算法比模糊C-均值算法迭代调整过程短,收敛速度快,聚类效果好。     

Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index

Context adaptation (CA) based on evolutionary algorithms is certainly a promising approach to the development of fuzzy rule-based systems (FRBSs). In CA, a context-free model is instantiated to a context-adapted FRBS so as to increase accuracy. A typical requirement in CA is that the context-adapted system maintains the same interpretability as the context-free model, a challenging constraint given that accuracy and interpretability are often conflicting objectives. Furthermore, interpretability is difficult to quantify because of its very nature of being a qualitative concept. In this paper, we first introduce a novel index based on fuzzy ordering relations in order to provide a measure of interpretability. Then, we use the proposed index and the mean square error as goals of a multi-objective evolutionary algorithm aimed at generating a set of Pareto-optimum context-adapted Mamdani-type FRBSs with different trade-offs between accuracy and interpretability. CA is obtained through the use of specifically designed operators that adjust the universe of the input and output variables, and modify the core, the support and the shape of fuzzy sets characterizing the partitions of these universes. Finally, we show results obtained by using our approach on synthetic and real data sets.     

A generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms

Segmentation of an image into regions and the labeling of the regions is a challenging problem. In this paper, an approach that is applicable to any set of multifeature images of the same location is derived. Our approach applies to, for example, medical images of a region of the body; repeated camera images of the same area; and satellite images of a region. The segmentation and labeling approach described here uses a set of training images and domain knowledge to produce an image segmentation system that can be used without change on images of the same region collected over time. How to obtain training images, integrate domain knowledge, and utilize learning to segment and label images of the same region taken under any condition for which a training image exists is detailed. It is shown that clustering in conjunction with image processing techniques utilizing an iterative approach can effectively identify objects of interest in images. The segmentation and labeling approach described here is applied to color camera images and two other image domains are used to illustrate the applicability of the approach.     

迁移模糊聚类在医学PET/MRI快速衰减校正中的应用

为了避免PET/CT对病人造成大剂量的X辐射伤害和更好地对PET/MRI混合成像系统进行信号衰减校正。在组织分割方法的指导下,利用迁移模糊聚类算法将对人体无伤害的磁共振成像(MRI)划分成诸如空气、液体、软组织、骨头等不同组织成分,然后赋予不同组织不同的线性衰减系数,以此来实现配准的PET成像的衰减校正工作。本方法具有三大好处:(1)迁移模糊聚类算法可以利用历史高级知识来辅助当前病人MRI组织分割任务,从而保证了临床有效性和鲁棒性,降低了环境噪声、数据缺失及个体解剖结构差异等因素对算法的不良影响;(2)本算法内嵌的基于迁移学习的简单抽样策略,在保证算法鲁棒性的同时,极大地缩短了聚类划分的整体时间,适用于医学MRI大数据快速聚类分割的场合,因而有效地增强了算法的实用性;(3)本算法涉及的历史MRI知识,都是通过历史MRI源数据高度总结得到,非历史MRI源数据,这有效地保护了病人隐私,符合医学诊断的基本要求。通过在真实数据集上的实验表明了上述优点。     

GSM churn management by using fuzzy c-means clustering and adaptive neuro fuzzy inference system

Churn management is important and critical issue for Global Services of Mobile Communications (GSM) operators to develop strategies and tactics to prevent its subscribers to pass other GSM operators. First phase of churn management starts with profile creation for the subscribers. Profiling process evaluates call detail data, financial information, calls to customer service, contract details, market details and geographic and population data of a given state. In this study, input features are clustered by x-means and fuzzy c-means clustering algorithms to put the subscribers into different discrete classes. Adaptive Neuro Fuzzy Inference System (ANFIS) is executed to develop a sensitive prediction model for churn management by using these classes. First prediction step starts with parallel Neuro fuzzy classifiers. After then, FIS takes Neuro fuzzy classifiers’ outputs as input to make a decision about churners’ activities.     

A multi-objective genetic optimization for fast,fuzzy rule-based credit classification with balanced accuracy and interpretability

Credit classification is an important component of critical financial decision making tasks such as credit scoring and bankruptcy prediction. Credit classification methods are usually evaluated in terms of their accuracy, interpretability, and computational efficiency. In this paper, we propose an approach for automatic designing of fuzzy rule-based classifiers (FRBCs) from financial data using multi-objective evolutionary optimization algorithms (MOEOAs). Our method generates, in a single experiment, an optimized collection of solutions (financial FRBCs) characterized by various levels of accuracy-interpretability trade-off. In our approach we address the complexity- and semantics-related interpretability issues, we introduce original genetic operators for the classifier's rule base processing, and we implement our ideas in the context of Non-dominated Sorting Genetic Algorithm II (NSGA-II), i.e., one of the presently most advanced MOEOAs. A significant part of the paper is devoted to an extensive comparative analysis of our approach and 24 alternative methods applied to three standard financial benchmark data sets, i.e., Statlog (Australian Credit Approval), Statlog (German Credit Approval), and Credit Approval (also referred to as Japanese Credit) sets available from the UCI repository of machine learning databases (http://archive.ics.uci.edu/ml). Several performance measures including accuracy, sensitivity, specificity, and some number of interpretability measures are employed in order to evaluate the obtained systems. Our approach significantly outperforms the alternative methods in terms of the interpretability of the obtained financial data classifiers while remaining either competitive or superior in terms of their accuracy and the speed of decision making.     

基于Q学习算法的变论域模糊控制新算法

变论域模糊控制器的控制函数被＂复制＂到后代中,往往存在着＂失真＂现象,这种现象的后果是造成算法本身的误差.针对这一问题,本文提出了一种基于Q学习算法的变论域模糊控制优化设计方法.本算法在变论域模糊控制算法基础上提出了一种利用伸缩因子、等比因子相互协调来调整论域的构想,且通过用Q学习算法来寻优参数使控制器性能指标最小,使其在控制过程中能够降低＂失真率＂,从而进一步提高控制器性能.最后,把算法运用于一个二阶系统与非最小相位系统,实验表明,该算法不但具有很好的鲁棒性及动态性能,且与变论域模糊控制器比较起来,其控制性能也更加提高.     

基于模糊推理的汽车辅助驾驶系统控制算法

在建立汽车辅助驾驶系统模型的基础上,指出满足驾驶员的驾驶特征是车辆控制的一个重要指标,此外针对驾驶员驾驶行为的不精确性,提出了以模糊推理为基础的上位控制方法,并对其进行了现场实验。实验结果表明,用模糊控制理论模拟驾驶行为的不精确性是可行的。通过模糊控制自车的速度,能够实现自车在多种工况下保持安全状态。     

基于减法聚类和自适应神经模糊推理系统的递阶模糊系统的设计

提出了一种设计递阶模糊系统的简易而有效的方法.在得到一个单级模糊系统的基础上,用灵敏度分析法对每一个输入变量的重要性进行排序,从而确定每一级子系统的输入变量.利用减法聚类和自适应神经 模糊推理系统逐级对子系统进行训练.所得到的递阶模糊系统可进一步得到简化.仿真实例证实了设计方法的有效性.     

Modeling and analysis of packing properties through a fuzzy inference system

Packing of manufactured products is important in protecting them from damage during handling and transportation. Several materials and methods are used for packing of products and the optimum level of packing materials should be determined to minimize damage to the product. Design and analysis of experiments (DOE) could be used for this. However, fuzzy logic models can be more suitable than mathematical models derived from DOE due to the error values. This is because fuzzy logic models use several functions instead of a single function. DOE and the adaptive neuro fuzzy inference system (ANFIS) modeling approaches are employed for the modeling and analysis of packing materials with the aim of delivering minimum damage. Although the root of mean square error (RMSE) values of the ANFIS model is 5.7622 × 10⁻⁶, the RMSE value of mathematical model from DOE is 3.57457. This result shows that the ANFIS model is more successful than the DOE model for this purpose.     

A class of low complexity high concurrence algorithms

A nonconventional approach to the analysis of dedicated computing structures in which the number of compute cycles is used as a design parameter to determine families of transformations implementable in the structure is presented. Using this approach, a single architecture can be used to implement a family of transformations with varying degrees of complexity. The transformations generated by a matrix multiplication array are considered in detail. It is shown that, for some real-time applications it becomes possible to incorporate the compute time as a constraint for designs based in optimality criteria. In particular, a least square approximation problem is discussed     

Sustainable supplier selection: A ranking model based on fuzzy inference system

In these days, considering the growth of knowledge about sustainability in enterprise, the sustainable supplier selection would be the central component in the management of a sustainable supply chain. In this paper the sustainable supplier selection criteria and sub-criteria are determined and based on those criteria and sub-criteria a methodology is proposed onto evaluation and ranking of a given set of suppliers. In the evaluation process, decision makers’ opinions on the importance of deciding the criteria and sub-criteria, in addition to their preference of the suppliers’ performance with respect to sub-criteria are considered in linguistic terms. To handle the subjectivity of decision makers’ assessments, fuzzy logic has been applied and a new ranking method on the basis of fuzzy inference system (FIS) is proposed for supplier selection problem. Finally, an illustrative example is utilized to show the feasibility of the proposed method.     

A VLSI fuzzy inference processor based on a discrete analogapproach

In this paper we present a design for a general-purpose fuzzy processor, the core of which is based on an analog-numerical approach combining the inherent advantages of analog and digital implementations, above all as regards noise margins. The architectural model proposed was chosen in such a way as to obtain a processor capable of working with a considerable degree of parallelism. The internal structure of the processor is organized as a cascade of pipeline stages which perform parallel execution of the processes into which each inference can be decomposed. A particular feature of the project is the definition of a `fuzzy-gate', which executes elementary fuzzy computations, on which construction of the whole core of the processor is based. Designed using CMOS technology, the core can be integrated into a single chip and can easily be extended. The performance obtainable, in the order of 50 Mega fuzzy rules per second, is of a considerable level