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New two-dimensional fuzzy C-means clustering algorithm for image segmentation 被引量:4
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作者 周鲜成 申群太 刘利枚 《Journal of Central South University of Technology》 EI 2008年第6期882-887,共6页
To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this... To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this method, the image segmentation was converted into an optimization problem. The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixels described by the improved two-dimensional histogram. By making use of the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center could be obtained by iterative optimization, and the image segmentation could be accomplished. The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%. The proposed algorithm has strong anti-noise capability, high clustering accuracy and good segment effect, indicating that it is an effective algorithm for image segmentation. 展开更多
关键词 image segmentation fuzzy c-means clustering particle swarm optimization two-dimensional histogram
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Power interconnected system clustering with advanced fuzzy C-mean algorithm 被引量:6
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作者 王洪梅 KIM Jae-Hyung +2 位作者 JUNG Dong-Yean LEE Sang-Min LEE Sang-Hyuk 《Journal of Central South University》 SCIE EI CAS 2011年第1期190-195,共6页
An advanced fuzzy C-mean (FCM) algorithm was proposed for the efficient regional clustering of multi-nodes interconnected systems. Due to various locational prices and regional coherencies for each node and point, m... An advanced fuzzy C-mean (FCM) algorithm was proposed for the efficient regional clustering of multi-nodes interconnected systems. Due to various locational prices and regional coherencies for each node and point, modified similarity measure was considered to gather nodes having similar characteristics. The similarity measure was needed to contain locafi0nal prices as well as regional coherency. In order to consider the two properties simultaneously, distance measure of fuzzy C-mean algorithm had to be modified. Regional clustering algorithm for interconnected power systems was designed based on the modified fuzzy C-mean algorithm. The proposed algorithm produces proper classification for the interconnected power system and the results are demonstrated in the example of IEEE 39-bus interconnected electricity system. 展开更多
关键词 fuzzy c-mean similarity measure distance measure interconnected system clustering
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Kernel method-based fuzzy clustering algorithm 被引量:2
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作者 WuZhongdong GaoXinbo +1 位作者 XieWeixin YuJianping 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2005年第1期160-166,共7页
The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, d... The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis. 展开更多
关键词 fuzzy clustering analysis kernel method fuzzy c-means clustering.
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Partition region-based suppressed fuzzy C-means algorithm 被引量:1
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作者 Kun Zhang Weiren Kong +4 位作者 Peipei Liu Jiao Shi Yu Lei Jie Zou Min Liu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2017年第5期996-1008,共13页
Aimed at the problem that the traditional suppressed fuzzy C-means clustering algorithms ignore the real needs of different objects, applying the same suppressed parameter for modifying membership degrees of all the o... Aimed at the problem that the traditional suppressed fuzzy C-means clustering algorithms ignore the real needs of different objects, applying the same suppressed parameter for modifying membership degrees of all the objects, a novel partition region-based suppressed fuzzy C-means clustering algorithm with better capacity of adaptability and robustness is proposed in this paper. The model based on the real needs of different objects is built, making it clear to decide whether to proceed with further determination; in addition, the external user-defined suppressed parameter is automatically selected according to the intrinsic structural characteristic of each dataset, making the proposed method become robust to the fluctuations in the incoming dataset and initial conditions. Experimental results show that the proposed method is more robust than its counterparts and overcomes the weakness of the original suppressed clustering algorithm in most cases. 展开更多
关键词 shadowed set suppressed fuzzy c-means clustering automatically parameter selection soft computing techniques
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Instance reduction for supervised learning using input-output clustering method
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作者 YODJAIPHET Anusorn THEERA-UMPON Nipon AUEPHANWIRIYAKUL Sansanee 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第12期4740-4748,共9页
A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input d... A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input data in accordance with the groups of output data.Then,a set of prototypes are selected from the clustered input data.The inessential data can be ultimately discarded from the data set.The proposed method can reduce the effect from outliers because only the prototypes are used.This method is applied to reduce the data set in regression problems.Two standard synthetic data sets and three standard real-world data sets are used for evaluation.The root-mean-square errors are compared from support vector regression models trained with the original data sets and the corresponding instance-reduced data sets.From the experiments,the proposed method provides good results on the reduction and the reconstruction of the standard synthetic and real-world data sets.The numbers of instances of the synthetic data sets are decreased by 25%-69%.The reduction rates for the real-world data sets of the automobile miles per gallon and the 1990 census in CA are 46% and 57%,respectively.The reduction rate of 96% is very good for the electrocardiogram(ECG) data set because of the redundant and periodic nature of ECG signals.For all of the data sets,the regression results are similar to those from the corresponding original data sets.Therefore,the regression performance of the proposed method is good while only a fraction of the data is needed in the training process. 展开更多
关键词 instance reduction input-output clustering fuzzy c-means clustering support vector regression supervised learning
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Integrated parallel forecasting model based on modified fuzzy time series and SVM 被引量:1
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作者 Yong Shuai Tailiang Song Jianping Wang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2017年第4期766-775,共10页
A dynamic parallel forecasting model is proposed, which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is ... A dynamic parallel forecasting model is proposed, which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly, the input data sets are optimized and their coherence is ensured, the region scale algorithm is modified and non-isometric multi scale region fuzzy time series model is built. At the same time, the particle swarm optimization algorithm about the particle speed, location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate. 展开更多
关键词 fuzzy c-means clustering fuzzy time series interval partitioning support vector machine particle swarm optimization algorithm parallel forecasting
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Fuzzy identification of nonlinear dynamic system based on selection of important input variables 被引量:1
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作者 LYU Jinfeng LIU Fucai REN Yaxue 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2022年第3期737-747,共11页
Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structur... Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the structure.Secondly, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indi cate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno(T-S) fuzzy modeling. 展开更多
关键词 Takagi-Sugeno(T-S)fuzzy modeling input variable selection(IVS) fuzzy identification fuzzy c-means clustering algorithm
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井下基于动态指纹更新的指纹定位算法研究 被引量:4
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作者 崔丽珍 王巧利 +1 位作者 郭倩倩 杨勇 《系统仿真学报》 CAS CSCD 北大核心 2021年第4期818-824,共7页
围绕煤矿井下环境特点,提出一种基于动态指纹更新的指纹定位算法。该算法运用FCM(Fuzzy C-Means Clustering)按信号分布特征划分井下定位区域,在各个子区域建立训练学习模型。在FCM算法基础上提出一种基于移动用户位置的HMM(Hidden Mark... 围绕煤矿井下环境特点,提出一种基于动态指纹更新的指纹定位算法。该算法运用FCM(Fuzzy C-Means Clustering)按信号分布特征划分井下定位区域,在各个子区域建立训练学习模型。在FCM算法基础上提出一种基于移动用户位置的HMM(Hidden Markov Model)运动信息序列模型,通过用户无意识地参与RSSI(Received Signal Strength Indication)序列的采集,实现指纹数据库的动态更新。运用具有自学习能力的ANFIS(Adaptive Network-based Fuzzy Inference System)算法定位未知节点。实验结果表明:所提的井下基于动态指纹更新的指纹定位算法定位精度可达2.6 m,满足煤矿井下巷道的实时定位需求。 展开更多
关键词 煤矿井下 指纹匹配定位 fuzzy c-means clustering算法 区域划分 指纹库更新 hidden Markov model运动轨迹模型 adaptive network-based fuzzy inference system定位模型 定位精度
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Adaptive WNN aerodynamic modeling based on subset KPCA feature extraction 被引量:4
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作者 孟月波 邹建华 +1 位作者 甘旭升 刘光辉 《Journal of Central South University》 SCIE EI CAS 2013年第4期931-941,共11页
In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel pr... In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles. 展开更多
关键词 WAVELET neural network fuzzy c-means clustering kernel principal components analysis feature extraction aerodynamic modeling
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A new measuring method for maximal length, width and thickness dimensions of coarse aggregates
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作者 段跃华 张肖宁 吴传海 《Journal of Central South University》 SCIE EI CAS 2011年第6期2150-2156,共7页
In order to establish a new method for measuring the dimensions of coarse aggregates, five different-size flat and elongated (F&E) coarse aggregates were glued into two specimens by epoxy resin, respectively, and ... In order to establish a new method for measuring the dimensions of coarse aggregates, five different-size flat and elongated (F&E) coarse aggregates were glued into two specimens by epoxy resin, respectively, and slice images were obtained by X-ray CT, then the aggregates were extracted by the fuzzy c-means clustering algorithm. Attributions of the particle on different cross-sections were determined by the ‘overlap area method’. And unified three-dimensional Cartesian coordinate system was established based on continuous slice images. The coefficient values of spherical harmonics descriptor representing particles surface profile were gained, then each scanned particle was represented by 60×120 discrete points conformably with spherical harmonics descriptor. The chord length and direction angles were determined by the calculation. With the major axis (L) and orthogonal axis (W and T), the calculated results were compared with those measured by caliper. It is concluded that the new L, W, and T dimension measuring method is able to take the place of the present manual measurement. 展开更多
关键词 coarse aggregate flat and elongated (F&E) aggregate X-ray CT digital image processing fuzzy c-means clustering overlap area method spherical harmonics
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