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A novel fault diagnosis scheme applying fuzzy clustering algorithms

APPLIED SOFT COMPUTING 2017年 58卷 605-619页

作者： Rodriguez Ramos, A. Llanes-Santiago, O. Bernal de lazaro, J. M. Cruz Corona, C. Silva Neto, A. J. Verdegay Galdeano, J. L. Univ Tecnol Habana Jose Antonio Echeverria Dept Automat & Computac Havana Cuba Univ Granada ETS Ingn Informat Dept Ciencias Computac & Inteligencia Artificial Granada Spain Univ Estado Rio de Janeiro IPRJ UERJ Inst Politecn Nova Friburgo Brazil

In this paper an approach to design data driven based fault diagnosis systems using fuzzy clustering techniques is presented. In this proposal, as a first part of the classification process, the data was preprocessed to eliminate outliers and reduce the confusion. To achieve this, the Noise clustering and Density Oriented fuzzy C-Means algorithms were used. Secondly, the Kernel fuzzy C-Means algorithm was used to achieve greater separability among the classes, and reduce the classification errors. Finally, a third step is developed to optimize the two parameters used in the algorithms in the training stage using the Differential Evolution algorithm. The proposed approach was validated using the nonlinear continuous stirred-tank reactor. The obtained results indicate the feasibility of the proposal. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Fault diagnosis fuzzy clustering algorithms fuzzy kernel algorithms Optimal parameters

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CLUSTER VALIDITY FOR fuzzy clustering algorithms

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fuzzy SETS AND SYSTEMS 1981年第2期5卷 177-185页

作者： WINDHAM, MP Department of Mathematics Utah State University Logan UT 84322 U.S.A.

The proportion exponent is introduced as a measure of the validity of the clustering obtained for a data set using a fuzzy clustering algorithm. It is assumed that the output of an algorithm includes a fuzzy nembership function for each data point. We show how to compute the proportion of possible memberships whose maximum entry exceeds the maximum entry of a given membership function, and use these proportions to define the proportion exponent. Its use as a validity functional is illustrated with four numerical examples and its effectiveness compared to other validity functionals, namely, classification entropy and partition coefficient.

关键词： fuzzy clustering algorithms Cluster validity Proportion exponent fuzzy isodata algorithm Entropy Partition coefficient

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Estimation of parameters in latent class models using fuzzy clustering algorithms

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2005年第2期160卷 515-531页

作者： Yang, MS Yu, NY Chung Yuan Christian Univ Dept Appl Math Chungli 32023 Taiwan

A mixture approach to clustering is an important technique in cluster analysis. A mixture of multivariate multinormal distributions is usually used to analyze categorical data with latent class model. The parameter estimation is an important step for a mixture distribution. Described here are four approaches to estimating the parameters of a mixture of multivariate multinomial distributions. The first approach is an extended maximum likelihood (ML) method. The second approach is based on the well-known expectation maximization (EM) algorithm. The third approach is the classification maximum likelihood (CML) algorithm. In this paper, we propose a new approach using the so-called fuzzy class model and then create the fuzzy classification maximum likelihood (FCML) approach for categorical data. The accuracy, robustness and effectiveness of these four types of algorithms for estimating the parameters of multivariate binomial mixtures are compared using real empirical data and samples drawn from the multivariate binomial mixtures of two classes. The results show that the proposed FCML algorithm presents better accuracy, robustness and effectiveness. Overall, the FCML algorithm has the superiority over the ML, EM and CML algorithms. Thus, we recommend FCML as another good tool for estimating the parameters of mixture multivariate multinomial models. (C) 2003 Elsevier B.V. All rights reserved.

关键词： categorical data latent class model fuzzy class model maximum likelihood function fuzzy clustering algorithms

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Increasing energy efficiency of rule-based fuzzy clustering algorithms using CLONALG-M for wireless sensor networks

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APPLIED SOFT COMPUTING 2021年 109卷 107510-107510页

作者： Sert, Seyyit Alper Yazici, Adnan Supreme Headquarters Allied Powers Europe SHAPE Strateg Dev & Preparat Directorate B-7010 Mons Belgium Nazarbayev Univ Sch Sci & Technol Dept Comp Sci Nur Sultan 010000 Kazakhstan Middle East Tech Univ Dept Comp Engn TR-06800 Ankara Turkey

Because of its efficiency, clustering is used for effective communication in Wireless Sensor Networks (WSNs). In the WSN clustering area, fuzzy approaches are found to be superior to crisp cluster counterparts when the boundaries between clusters are unclear. As a result, many studies have proposed some fuzzy-based solutions to the cluster problem in WSNs. Most rule-based fuzzy clustering systems employ field experts in trial and error processes, identifying and defining fuzzy rules as well as the forms of membership functions at the output;thus, considerable time has been allocated to realize and define these functions. Therefore, it is almost impossible or impractical to achieve a fuzzy system optimally. In this study, we propose a modified clonal selection algorithm (CLONALG-M) to improve the energy efficiency of rule-based fuzzy clustering algorithms. Although some studies in the literature focus on fuzzy optimization in general, to the best of our knowledge, performance improvement of rule-based fuzzy clustering algorithms is not taken into account. The CLONALG-M algorithm based on the Clonal Selection Principle is used to elucidate the basic principles of an adaptive immune system. In this study, we apply this principle to determine the approximate deployment of outputbased membership functions that increase the performance of rule-based fuzzy clustering algorithms, whose rule base and shape of membership functions are previously known. Experimental analysis and evaluations of the proposed approach have been performed on selected fuzzy clustering approaches, and obtained results show that our approach performs and adapts well for improving performance of fuzzy output functions. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Clonal selection principle Performance tuning fuzzy function approximation fuzzy clustering algorithms Wireless sensor networks

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Grounding Faulted Feeder Detection Based on fuzzy clustering algorithms

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International Conference on Information Technology for Manufacturing Systems (ITMS 2011)

作者： Wang Yuanyuan Jian Jinbao Zeng Xiangjun Long Lihong Guangxi Univ Nanning Peoples R China Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha Hunan Peoples R China Hunan Elect Power Design Inst Changsha Hunan Peoples R China

ISBN: (纸本)9783037851494

Many power distribution systems have been operated with neutral ineffectively earthed, and earth fault current is no more than a few tens of amperes. Traditional earth fault detection methods based on zero sequence current has poor precision and sensitivity in this case. For improvement, a novel principle for feeder grounding fault protection based on fuzzy clustering algorithms is presented in this paper. First, the historical data are divided into two groups by fuzzy clustering algorithms. The space relative distance among detected pattern and two cluster centers is then calculated to discriminate the faulted feeder. It can detect high impedance grounding faults (HIGF). The scheme has been verified by EMTP simulation, and results show that the proposed scheme always trips all kinds of grounding faults with high sensitivity and robustness in neutral ineffectively earthed power systems (NIEPS).

关键词： distribution network high impedance grounding faults (HIGF) fault detection fuzzy clustering algorithms neutral ineffectively earthed power systems (NIEPS)

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A generalized fuzzy clustering regularization model with optimality tests and model complexity analysis

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IEEE TRANSACTIONS ON fuzzy SYSTEMS 2007年第5期15卷 904-915页

作者： Yu, Jian Yang, Miin-Shen Beijing Jiaotong Univ Dept Comp Sci Inst Comp Sci Beijing 100044 Peoples R China Chung Yuan Christian Univ Dept Appl Math Chungli 32023 Taiwan

In this paper, we propose a generalized fuzzy clustering regularization (GFCR) model and then study its theoretical properties. GFCR unifies several fuzzy clustering algorithms, such as fuzzy c-means (FCM), maximum entropy clustering (MEC), fuzzy clustering based on Fermi-Dirac entropy, and fuzzy bidirectional associative clustering network, etc. The proposed GFCR becomes an alternative model of the generalized FCM (GFCM) that was recently proposed by Yu and Yang. To advance theoretical study, we have the following three considerations. 1) We give an optimality test to monitor if GFCR converges to a local minimum. 2) We relate the GFCR optimality tests to Occam's razor principle, and then analyze the model complexity for fuzzy clustering algorithms. 3) We offer a general theoretical method to evaluate the performance of fuzzy clustering algorithms. Finally, some numerical experiments are used to demonstrate the validity of our theoretical results and complexity analysis.

关键词： fuzzy clustering algorithms fuzzy c-means (FCM) generalized fuzzy clustering regularization (GFCR) model optimality test occam's razor principle model complexity parameter selection

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Black Hole Entropic fuzzy clustering

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2018年第9期48卷 1622-1636页

作者： Liu, Jiefang Chung, Fu-Lai Wang, Shitong Jiangnan Univ Sch Digital Media Wuxi 214122 Peoples R China Hubei Commun Tech Coll Sch Traff Informat Wuhan 430079 Hubei Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China

clustering is a common approach for finding the intrinsic pattern structure embedded in unlabeled data. In this paper, a new clustering algorithm, called black hole entropic fuzzy clustering (BHEFC), is presented to absorb the merits of three aspects: 1) black hole entropy (BHE)-based information theory;2) fuzzy clustering;and 3) Bayesian inference model. First, through the link between clustering and the black hole phenomenon in astrophysics, the minimum BHE criterion for fuzzy clustering is proposed. Then, it is revealed that BHE-based fuzzy clustering can be realized by using a maximum-a-posteriori (MAP) framework, which in fact indicates that fuzziness and probability can co-jointly work in a collaborative rather than repulsive way. According to the proposed MAP framework for BHEFC, the fuzzifier m, which is limited to be less than 1 rather than bigger than 1 in fuzzy c-means, can be explained as the partition accuracy in the form of (1-m) for a dataset, and the fuzzy memberships and clustering centers can be determined in a probabilistic inference way, by means of iterative sampling with the assumption of the Dirichlet distribution of the fuzzy memberships. The incremental version of the proposed fuzzy clustering model is also developed here. Experimental results on synthetic and real datasets and images for segmentation demonstrate the improved clustering results of the proposed algorithms over the comparison algorithms.

关键词： Bayesian inference framework black hole entropy (BHE) fuzzy clustering algorithms probabilistic clustering model

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Gaussian Collaborative fuzzy C-Means clustering

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INTERNATIONAL JOURNAL OF fuzzy SYSTEMS 2021年第7期23卷 2218-2234页

作者： Gao, Yunlong Wang, Zhihao Li, Huidui Pan, Jinyan Xiamen Univ Dept Automat Xiamen Peoples R China Jimei Univ Coll Informat Engineer Xiamen Peoples R China

For most FCM-based fuzzy clustering algorithms, several problems, such as noise, non-spherical clusters, and size-imbalanced clusters, are difficult to solve. Different fuzzy clustering algorithms are developed to deal with these problems from different perspectives. However, no comprehensive viewpoint to generalize these problems has been put forward. In this paper, we reveal the inherent deficiency of FCM and propose a new fuzzy clustering method called Gaussian Collaborative fuzzy C-means (GCFCM) to solve these problems. In GCFCM, Gaussian mixture model (GMM) and collaborative technology are adopted to enhance the ability of recognizing the intrinsic structure of clusters. Experimental results confirm that GCFCM is effective in dealing with noise, non-spherical clusters, size-imbalanced clusters, and those also show excellent performance in dealing with real-world data sets.

关键词： fuzzy c-means (FCM) Gaussian collaborative fuzzy clustering algorithms Gaussian mixture model (GMM)

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ON THE LOCAL OPTIMALITY OF THE fuzzy ISODATA clustering-ALGORITHM

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1986年第2期8卷 284-288页

作者： SELIM, SZ ISMAIL, MA UNIV WINDSOR SCH COMP SCIWINDSOR N9B 3P4ONTARIOCANADA

The convergence of the fuzzy ISODATA clustering algorithm was proved by Bezdek [3]. Two sets of conditions were derived and it was conjectured that they are necessary and sufficient for a local minimum point. In this paper, we address this conjecture and explore the properties of the underlying optimization problem. The notions of reduced objective function and improving and feasible directions are used to examine this conjecture. Finally, based on the derived properties of the problem, a new stopping criterion for the fuzzy ISODATA algorithm is proposed.

关键词： clustering algorithms Hilbert space Filtering Speech processing Multidimensional signal processing Systems engineering and theory Kalman filters Signal processing algorithms Petroleum Minerals Author Keywords local optimality fuzzy clustering algorithms fuzzy c-means algorithm fuzzy ISODATA algorithm fuzzy unsupervised classification

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CLUSTER VALIDITY FOR THE fuzzy C-MEANS clustering-ALGORITHM

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1982年第4期4卷 357-363页

作者： WINDHAM, MP Department of Mathematics Utah State University Logan UT 84322

The uniform data function is a function which assigns to the output of the fuzzy c-means (Fc-M) or fuzzy isodata algorithm a number which measures the quality or validity of the clustering produced by the algorithm. For the preselected number of cluster c, the Fc-M algorithm produces c vectors in the space in which the data lie, called cluster centers, which represent points about which the data are concentrated. It also produces for each data point c-membership values, numbers between zero and one which measure the similarity of the data points to each of the cluster centers. It is these membership values which indicate how the point is classified. They also indicate how well the point has been classified, in that values close to one indicate that the point is close to a particular center, but uniformly low memberships indicate that the point has not been classified clearly. The uniform data functional (UDF) combines the memberships in such a way as to indicate how well the data have been classified and is computed as follows. For each data point compute the ratio of its smallest membership to its largest and then compute the probability that one could obtain a smaller ratio (indicating better classification) from a clustering of a standard data set in which there is no cluster structure. These probabilities are then averaged over the data set to obtain the values of the UDF.

关键词： clustering algorithms fuzzy sets Object detection Information analysis Algorithm design and analysis Particle measurements Mathematics Author Keywords uniform data functional (UDF) Cluster validity functional fuzzy clustering algorithms fuzzy c-means (Fc-M) fuzzy sets

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