Partial discharge pattern classification using the fuzzy decision tree approach

Partial discharge (PD) measurement is a proven flaw detection technique for finding cavities that are defects in the insulating material. In this paper, a novel approach for the classification of cavity sizes, based on their maximum PD charge transfer-applied voltage (/spl Delta/Q-V) characteristics using a fuzzy decision tree system, is proposed. The (/spl Delta/Q-V) partial discharge patterns for different cavity sizes are represented by features extracted from their pulse shapes, and the classification rules are directly extracted from the data using the decision tree. The decision rules obtained from the decision tree are then converted to the fuzzy IF-then rules, and the back-propagation algorithm is utilized to tune the parameters of the membership functions employed in the fuzzy classifier. The neuro-fuzzy classification technique is shown to provide successful classification of void sizes in an easily interpretive fashion.     

A hybrid SVM and supervised learning approach to fuzzy min‐max hyperbox classifiers

Abstract

In this paper, a fuzzy min‐max hyperbox classifier is designed to solve M‐class classification problems using a hybrid SVM and supervised learning approach. In order to solve a classification problem, a set of training patterns is gathered from a considered classification problem. However, the training set may include several noisy patterns. In order to delete the noisy patterns from the training set, the support vector machine is applied to find the noisy patterns so that the remaining training patterns can describe the behavior of the considered classification system well. Subsequently, a supervised learning method is proposed to generate fuzzy min‐max hyperboxes for the remaining training patterns so that the generated fuzzy min‐max hyperbox classifier has good generalization performance. Finally, the Iris data set is considered to demonstrate the good performance of the proposed approach for solving this classification problem.     

Parameterization of a fuzzy classifier for the diagnosis of an industrial process

The aim of this paper is to present a classifier based on a fuzzy inference system. For this classifier, we propose a parameterization method, which is not necessarily based on an iterative training. This approach can be seen as a pre-parameterization, which allows the determination of the rules base and the parameters of the membership functions. We also present a continuous and derivable version of the previous classifier and suggest an iterative learning algorithm based on a gradient method. An example using the learning basis IRIS, which is a benchmark for classification problems, is presented showing the performances of this classifier. Finally this classifier is applied to the diagnosis of a DC motor showing the utility of this method. However in many cases the total knowledge necessary to the synthesis of the fuzzy diagnosis system (FDS) is not, in general, directly available. It must be extracted from an often-considerable mass of information. For this reason, a general methodology for the design of a FDS is presented and illustrated on a non-linear plant.     

模糊粗糙集在轴承故障模式识别中的应用

提出一种基于模糊粗糙集理论的模式识别方法,将动态聚类法和方差分析法引入连续属性模糊化,获取模糊隶属函数,避开了粗糙集理论属性离散化过程带来的信息丢失;利用F检验判断分类的合理性,克服了人为确定分类数目的缺点;应用模糊化得到的模糊决策表进行条件属性约简,通过属性值约简,提取了清晰、简明的故障模式规则。轴承故障模式识别结果表明,该方法对比一般粗糙集理论,有效地提高了模式识别精度,在实际模式识别中具有很好的应用价值。     

A new query expansion method for document retrieval based on the inference of fuzzy rules

Abstract

Automatic query expansion based on user relevance feedback techniques can improve the performance of document retrieval systems. In this paper, we present a new query expansion method based on the inference of fuzzy rules and user relevance feedback techniques to deal with document retrieval. The proposed method uses membership functions and fuzzy rules to infer relevant degrees of expansion terms and puts the expansion terms with larger relevant degrees into the original user's query. Then, the system calculates the degree of similarity of each document with respect to the expanded user's query. The proposed method gets a higher average precision rate and a higher average recall rate than the existing methods for document retrieval.     

机构选型的改进多级模糊综合评判

 机构选型多级模糊评判的核心计算是实现隶属度转换；但是,现有隶属度转换方法包含冗余性,表现在指标隶属度中对目标分类不起作用的冗余部分也被用于计算目标隶属度.为此,用基于熵的数据挖掘方法,通过挖掘隐藏在各指标隶属度中关于目标分类的知识信息定义指标区分权；用区分权清除指标隶属度中对目标分类不起作用的冗余数值并提取有效值；有效值经指标重要性权重转化为可比值；用可比值计算目标隶属度实现隶属度转换.由此建立机构选型的改进模糊评判模型.     

Ranking alternatives via maximizing set and minimizing set based fuzzy MCDM approach

Abstract

This work suggests a maximizing set and minimizing set based fuzzy multiple criteria decision‐making (MCDM) model, where criteria are classified into cost and benefit criteria. The final fuzzy evaluation value of each alternative is developed based on the concept of subtracting the summation of weighted normalized benefit ratings from that of weighted normalized cost ratings. Using interval arithmetic of fuzzy numbers can develop the membership functions for the final fuzzy evaluation values. Chen's maximizing set and minimizing set is then applied to defuzzify all the final fuzzy numbers for ranking alternatives. Formulas for the membership functions and ranking procedure of the final fuzzy numbers are clearly presented. The suggested method provides an extension to the fuzzy MCDM techniques available. A numerical example demonstrates the computational process of the proposed method.     

Constructing a new fuzzy classifier based on hierarchical fuzzy entropy

Abstract

In an earlier work, Lee et al. (Lee et al., 2001) presented a simple and fast fuzzy classifier that employed fuzzy entropy to evaluate pattern distribution information in a pattern space. In this paper, we extend his work to propose a new fuzzy classifier based on hierarchical fuzzy entropy (FC‐HFE). We retained the main parts of the original structure and modified some methods (e.g., methods for deciding the number of intervals in each dimension and for assigning class labels). In addition, the hierarchical fuzzy entropy is proposed for partitioning the decision region. The proposed FC‐HFE improves classification accuracy and overcomes some of the drawbacks in the Lee et al method (Lee et al., 2001). The simulation results show that the classification rate of the proposed FC‐HFE is better than earlier methods.     

Cloud manufacturing resources fuzzy classification based on genetic simulated annealing algorithm

To solve the problem of fuzzy classification of manufacturing resources in a cloud manufacturing environment, a hybrid algorithm based on genetic algorithm (GA), simulated annealing (SA) and fuzzy C-means clustering algorithm (FCM) is proposed. In this hybrid algorithm, classification is based on the processing feature and attributes of the manufacturing resource; the inner and outer layers of the nested loops are solving it, GA obtains the best classification number in the outer layer; the fitness function is constructed by fuzzy clustering algorithm (FCM), carrying out the selection, crossover and mutation operation and SA cooling operation. The final classification results are obtained in the inner layer. Using the hybrid algorithm to solve 45 kinds of manufacturing resources, the optimal classification number is 9 and the corresponding classification results are obtained, proving that the algorithm is effective.     

Ensemble of randomized soft decision trees for robust classification

For classification, decision trees have become very popular because of its simplicity, interpret-ability and good performance. To induce a decision tree classifier for data having continuous valued attributes, the most common approach is, split the continuous attribute range into a hard (crisp) partition having two or more blocks, using one or several crisp (sharp) cut points. But, this can make the resulting decision tree, very sensitive to noise. An existing solution to this problem is to split the continuous attribute into a fuzzy partition (soft partition) using soft or fuzzy cut points which is based on fuzzy set theory and to use fuzzy decisions at nodes of the tree. These are called soft decision trees in the literature which are shown to perform better than conventional decision trees, especially in the presence of noise. Current paper, first proposes to use an ensemble of soft decision trees for robust classification where the attribute, fuzzy cut point, etc. parameters are chosen randomly from a probability distribution of fuzzy information gain for various attributes and for their various cut points. Further, the paper proposes to use probability based information gain to achieve better results. The effectiveness of the proposed method is shown by experimental studies carried out using three standard data sets. It is found that an ensemble of randomized soft decision trees has outperformed the related existing soft decision tree. Robustness against the presence of noise is shown by injecting various levels of noise into the training set and a comparison is drawn with other related methods which favors the proposed method.     

用于水声目标识别的自适应遗传样本选择算法

针对训练样本集中含有噪声样本、冗余样本以及无关样本,导致分类系统分类性能下降、不稳定的水声目标识别问题,提出了一种新的自适应遗传样本选择算法(Adaptive Genetic Instance Selection Algorithm, AGISA)。算法先随机生成初始种群,接着利用设计的遗传算子(跨代选择、自适应交叉和简化最近邻变异)指导种群进化,每代中对分类贡献大且选择样本数目少的个体适应度值高。提取了实测3类水声目标的多域特征,进行样本选择和分类识别仿真实验,结果表明：AGISA可以选出有效样本子集,在样本维数下降约73%的情况下,支持向量机分类器的正确分类率能提高约2.5%;并且AGISA具有较好的收敛性、稳定性,所得优化样本子集具有较好泛化能力且能明显减少分类的时间。     

Linear defuzzified output of fuzzy controllers via mixed fuzzy logic operators

Abstract

The linear defuzzified output of a fuzzy controller with two fuzzy variable inputs and one output is discussed in this paper. Arbitrary amounts of triangular fuzzy numbers are employed to fuzzify the linguistic variables in fuzzy control rules. We show that the defuzzified output is exactly equivalent to a linear function of the inputs to the fuzzy controller by using three mixed fuzzy logic operators to evaluate the control rules.     

Neuro-fuzzy network for flavor recognition and classification

This paper presents the neuro-fuzzy Takagi-Sugeno-Kang (TSK) network for the recognition and classification of flavor. The important role in this process fulfills the self-organizing process used for the creation of the inference rules. The self-organizing neurons perform the role of clustering data into fuzzy groups with different membership values (the preprocessing stage). Applying the automatic control of clusters, we have the optimal size of the TSK network. The developed measuring system has been applied for the recognition of flavor of different brands of beer. The fuzzy neural network is used for processing signals obtained from the semiconductor sensor array. The results of numerical experiments have confirmed the excellent performance of such solutions.     

Intuitionistic fuzzy approach for enhancement of low contrast mammogram images

Mammogram image enhancement is very much necessary in diagnosing breast cancer or tumor at an early stage. Nonuniform illumination and low contrast images are commonly encountered in mammogram images. Conventional enhancement algorithms produce either some artifacts or cannot highlight minute details present in the images, particularly when dealing with mammogram images. In this article, we propose a new mammogram image enhancement scheme using Atanassov's intuitionistic fuzzy set (IFS) theory. IFS considers two uncertainties—membership and nonmembership degree apart from membership degree as in fuzzy set theory. As mammogram images are low contrast images and many of the image definitions are vague/unclear, so IFS theory may be suitable for better image enhancement. Initially, the image is transformed to an intuitionistic fuzzy image using a novel intuitionistic fuzzy generator. Hesitation degree is computed and using the hesitation degree, two membership levels are computed to form an interval type 2 fuzzy set. These two membership functions are then combined using Zadeh's fuzzy t-conorm to form a new membership function. Threshold of interval type 2 fuzzy image is obtained using restricted equivalence function. Using the threshold, modified fuzzy hyperbolization is carried out. Real data experiments demonstrate that the proposed algorithm has better performance on contrast and visual quality of the images both quantitatively and qualitatively when compared with different existing methods.     

一种粗模糊神经分类器

介绍一种新的粗集编码模糊神经分类器。基于粗集理论的概念，讨论了知识编码、属性简化、分类系统简化的方法；并利用模糊隶属度函数将输入精确信息映射为模糊变量信息，解决分类中病态定义的数据问题和提高系统非线性映射的分类能力；提出了结合系统参数的重要性因子的网络的模糊推理方法和粗模糊神经分类器的网络结构以及有导师的最小平方误差学习训练算法。实现的粗集编码模糊神经分类器具有网络结构空间维数低、学习算法简单、网络训练时间短、非线性特性丰富等优点。     

Fuzzy rules extraction using a hybrid of base‐n‐number‐coded GA and SVD‐QR

Abstract

A hybrid of a base‐n‐number‐coded genetic algorithm (base‐n‐number‐coded GA) and an SVD‐QR is proposed to construct a fuzzy system directly from some gathered input‐output data of the identified system. Each individual in the base‐n‐number‐coded GA is applied to determine the fuzzy sets in each input variable. However, the grid‐type fuzzy partition by the fuzzy sets associated with each input variable may generate some redundant fuzzy subspaces. Therefore, an SVD‐QR method is applied to remove the redundant fuzzy subspaces to efficiently describe the behavior of the identified system so that the premise part of the fuzzy system is determined. Then, the recursive least‐squares method is used to determine the consequent part of the fuzzy system. Subsequently, a fitness function is defined such that it can guide the search procedure to select an appropriate fuzzy system that not only maintains a good performance but also has relevant fuzzy rules. Finally, two nonlinear system identification problems are used to illustrate the efficiency of the proposed method.     

Multicriteria fuzzy optimization of structural systems

This paper proposes an efficient, systematic and generalized multicriteria fuzzy optimum design method for the structural systems, using the suboptimization concept, introduction of measure membership functions and fuzzy decision‐making techniques. Each objective function is suboptimized first for all discrete sets of common design variables and design parameters. In order to make the relative evaluation of suboptimized data of objective functions rationally and systematically, and involve the fuzziness or tolerance in the decision‐making process and design emphases, the measure membership functions are introduced for all objective functions. The membership functions of the suboptimized objective functions are determined systematically using the corresponding measure membership functions as datum. A hybrid decision‐making process is developed combining the weighted operator method, comparison processes of maximum membership values and backward interpolation processes for the determination of the global optimum solution. The weighted operator method can also involve the relative emphases of each objective function simply. A design example of a practical prestressed concrete bridge system, in which the total expected cost after an earthquake and the aesthetics of the bridge system are the primary objectives, clarifies the applicability to any convex and non‐convex design problems, rationality, systematic design process and efficiency of the proposed design method. Copyright © 1999 John Wiley & Sons, Ltd.     

Hybrid neuro-fuzzy approach to the generation of measuring points for knowledge-based inspection planning

A knowledge-based inspection planning system is presented that can generate effective and consistent inspection plans automatically. The knowledge-based inspection planning system integrates part geometry information, tolerance information and heuristic knowledge of experienced inspection planners to determine the numbers and positions of measurement points. The system receives the tolerance information from users and stores it in the common database with 3D CAD geometry. A set of fuzzy rules and membership functions is automatically extracted from historic learning data using a hybrid neuro-fuzzy method. After the fuzzy rules are generated by the hybrid neuro-fuzzy model, a genetic algorithm is applied to optimize the weight parameters to find the best values for the constants. The proposed knowledge-based inspection planning system provides the stable and consistent inspection plan by removing the subjectivity of a human planner.     

模糊几何特征及其在人造目标检测中的应用

提出具有模糊测度的几何基元特征的人造目标检测方法。根据相交直线段所组合构成的几何基元夹角的模糊测度,将单纯的几何基元特征扩展为模糊几何特征。应用方向算子检测图像边缘,并把边缘拟合成直线段集合;直线段集合扩展成几何基元集合,并根据所定义的模糊隶属函数,用几何基元的夹角构造成基元线段的隶属度列表;通过判断每个子区域内所有线段隶属度之和的最大值定位人造目标。检测自然背景下人造目标的实验表明,该方法检测率高达97％,整个算法的时间复杂度为O（n^2）,并有很好的稳定性且易硬件实现。     

A membership function approach to lot size re-order point inventory problems in fuzzy environments

Inventory models play an important role in logistics and supply chain management for reducing cost and increasing customer satisfaction. This paper develops an approach to derive the fuzzy objective value and decision variables of the fuzzy lot size re-order point inventory problem with parameters being fuzzy numbers and the shortages are backordered with extra cost incurred. Different from the existing studies, the idea is based on Zadeh's extension principle. A pair of mixed integer nonlinear programs (MINLP) parameterised by the possibility level α is formulated to calculate the lower and upper bounds of the minimal total cost per unit time at α, through which the membership function of the minimal total cost per unit time is constructed. At the same time the membership functions of the optimal order quantity and the optimal re-order point are also provided. A numerical example studied by previous studies is solved successfully to demonstrate the validity of the proposed method. Compared with previous studies, the obtained results which precisely and completely conserve the fuzziness of the input information are more informative for finding the best inventory policy since they are expressed by membership functions rather than by crisp ones. Moreover, to provide representative crisp solutions for designing inventory systems, the Yager's ranking index method is adopted to defuzzify the obtained membership functions. The successful extension of inventory models to fuzzy environments permits inventory models to have wider applications in practice.