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clustering Validity Function Fusion Method of fcm clustering algorithm Based on Dempster-Shafer Evidence Theory

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2022年第1期24卷 650-675页

作者： Wang, Hong-Yu Wang, Jie-Sheng Wang, Guan Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

With the deepening of the research on clustering algorithm, clustering validity has become an indispensable part of cluster analysis. However, due to the complexity of data structure and different attributes, any clustering validity function cannot be applied to all datasets, so clustering validity function has been bringing forth new ones. Therefore, this paper proposes a clustering validity function fusion model based on D-S evidence theory (DS-CVFFM), which adopts fcm clustering algorithm as the base algorithm, calculates the values of different validity functions, and then uses the values of different clustering validity functions as the evidence source to construct the basic probability assignment function (BPA). Finally, it integrates with the fusion rules of D-S evidence theory, and outputs the optimal clustering number according to the decision conditions. DS-CVFFM uses the information fusion of multiple clustering validity functions to judge the number of optimal clusters without the need to propose complex validity functions, and avoid the influence of expert factors in the weighted combination clustering validity evaluation method. Finally, 4 sets of artificial datasets and 14 sets of UCI datasets are selected to verify the effectiveness of the proposed model. The experimental results show that compared with the traditional clustering validity evaluation methods, the proposed fusion model has a significant improvement in the accuracy of judging the optimal number of clusters, and the stability is improved under different values of fuzzy exponent, which can overcome the shortcomings of traditional clustering validity evaluation methods.

关键词： D-S evidence theory fcm clustering algorithm clustering validity function Information fusion

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Cancellation of nonlinear distortion based on integration of fcm clustering algorithm and adaptive-two-stage linear approximation

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The Journal of China Universities of Posts and Telecommunications 2014年第3期21卷 18-22页

作者： WANG Gui-ye ZOU Wei-xia WANG Zhen-yu DU Guang-long GAO Ying Key laboratory of wireless universal communications Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing University of Posts and Telecommunications

A hybrid system of the fuzzy c-means （fcm） clustering algorithm and adaptive-two-stage linear approximation was presented for nonlinear distortion cancellation of radio frequency （RF） power amplifier （PA）. This mechanism can effectively eliminate noise, adaptively model PA＇s instantaneous change, and efficiently correct nonlinear distortion. This article puts forward the fcm clustering algorithm for clustering received signals to eliminate white noise, and then uses the adaptive-two-stage linear approximation to fit the inverse function of the amplitude＇s and phase＇s nonlinear mapping during the training phase. Parameters of the linear function and similarity function are trained using the gradient-descent and minimum mean-square error criteria. The proposed approach＇s training results is directly employed to eliminate sampling signal＇s nonlinear distortion. This hybrid method is realized easier than the multi-segment linear approximation and could reduce the received signal＇s bit error rate （BER） more efficiently.

关键词： PA nonlinear distortion cancellation fcm clustering algorithm similarity function adaptive-two-stage linear approximation

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Crack Monitoring for Hot-Spot Areas Under Time-Varying Load Condition Based on fcm clustering algorithm

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IEEE ACCESS 2019年 7卷 118850-118856页

作者： Sun, Hu Yi, Junyan Xu, Yu Wang, Yishou Qing, Xinlin Xiamen Univ Sch Aerosp Engn Xiamen 361005 Fujian Peoples R China

Crack monitoring for damage-prone structural area, named hot-spot, is one of the most interesting concerned technologies for aircraft structural health monitoring. This paper proposes an efficient technique based on Fuzzy c-means (fcm) clustering algorithm to monitor the crack growth under time-varying load condition. The proposed method first extracts damage indexes from the collected signals to construct the baseline model as well as the online fcm model. Migration index of the online model is calculated by Kullback-Liebler Divergence (KLD) and Bhattacharyya Distance (BD), respectively. By comparing with the Gaussian mixture model (GMM), experimental results show that the proposed fcm algorithm has less computation time and its migration index curve is smoother when the crack size is small. Furthermore, KLD achieves a higher sensitivity than BD as crack grows.

关键词： Crack monitoring time-varying load fcm clustering algorithm KLD migration index

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An fcm clustering algorithm based on the identification of accounting statement whitewashing behavior in universities

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JOURNAL OF INTELLIGENT SYSTEMS 2022年第1期31卷 345-355页

作者： Yang, Qihao Henan Polytech Inst Nanyang 473000 Peoples R China

The traditional recognition method of whitewash behavior of accounting statements needs to analyze a large number of special data samples. The learning rate of the algorithm is low, resulting in low recognition accuracy. To solve the aforementioned problems, this article proposes a method to identify the whitewash behavior of university accounting statements based on the fcm clustering algorithm. This article analyzes the motivation of university accounting statement whitewashing behavior, studies the common means of statement whitewashing, and establishes a fuzzy set for the identification of university accounting statement whitewashing behavior. By calculating the fuzzy partition coefficient, the membership matrix of whitewash behavior recognition is established, and the whitewash behavior is classified through the iteration of the fcm algorithm. The comparative experimental results show that the recognition method has good recognition performance, low recognition error rate, and recognition accuracy of 82%.

关键词： fcm clustering algorithm university accounting statements whitewashing behavior behavior recognition

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A Study of fcm clustering algorithm based on Interval Multiple Attribute Information

A Study of FCM Clustering Algorithm based on Interval Multip...

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2nd International Conference on Advances in Computational Modeling and Simulation (ACMS 2013)

作者： Guo, Li Liu, Guofeng Bao, Yu'e Inner Mongolia Univ Nationalities Coll Math Tongliao 028043 Peoples R China

ISBN: (纸本)9783037859063

In multiple attribute clustering algorithms with uncertain interval numbers, most of the distances between the interval-valued vectors only consider the differences of each interval endpoint ignoring a lot of information. To solve this problem, according to the differences between corresponding points in each interval number, this paper gives a distance formula between interval-valued vectors, extends a fcm clustering algorithm based on interval multiple attribute information. Through an example, we prove the validity and rationality of the algorithm.

关键词： interval-valued vector fcm clustering algorithm distance measure fuzzy partition

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CVT Measurement Error Range Recognition based on the fcm clustering algorithm 7

CVT Measurement Error Range Recognition based on the FCM Clu...

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7th International Conference on Energy, Electrical and Power Engineering (CEEPE)

作者： Liu, Yingshu Zhang, Yi Zhou, Feng Xu, Baipeng Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China State Grid Chongqing Elect Power Co Mkt Serv Ctr Chongqing Peoples R China Nankai Univ Coll Comp Sci Tianjin Peoples R China

ISBN: (纸本)9798350375794;9798350375800

Traditional capacitive mutual inductor (CVT) detection methods require error comparison between standard mutual inductor and evaluated mutual inductor in case of power outage. Due to power outage and repeated disassembly and installation of mutual inductors, this method of detection is cumbersome and inefficient. For this reason, a CVT measurement error range identification method based on fcm clustering algorithm is presented, that is, under the condition of power outage, the error range level of CVT itself is identified by clustering analysis of the secondary-side data of CVT to lock the sensor that needs power outage inspection as soon as possible. First, the secondary-side CVT data is segmented by rolling time window method, then the standard secondary-side CVT data in rolling time window is processed by fcm clustering of two cluster centers, and the average distances between the two cluster centers are obtained. Finally, the fcm clustering of two cluster centers for the second-sided CVT data in the rolling time window is performed to get the standard ratio of the distance between the cluster centers in each time window to identify the error range level. On this basis, the CVT with higher error level is detected for power outage. Experiment shows that this method can effectively identify the error range rank of CVT, thereby significantly improving its detection efficiency.

关键词： rolling time window method fcm clustering algorithm error evaluation capacitive voltage transformer

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Research on Module Diviosion Method for Product Based on fcm clustering algorithm

Research on Module Diviosion Method for Product Based on FCM...

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2011 International Conference on Control, Automation and Systems Engineering(CASE)

作者： Yang Yanan Miao Jinghong Wang Zhongwei School of Information and Communication Engineering Tianjin Polytechnic University

ISBN: (纸本)9781457708602

From the analysis of the product of configuration values that are comprehensive consideration,the customer demand and customize its relevance and principles according to relevant factors and the establishment of a module of spare parts into a matrix with fcm clustering a module are divided on the delimitation of entropy,and to division of schemes modules on preferred.

关键词： module division production configuration correlation matrix fcm clustering algorithm normalized partition entropy

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The Application of Improved fcm clustering algorithm in Behavior Recognition

The Application of Improved FCM Clustering Algorithm in Beha...

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2014年国际计算机科学与软件工程学术会议

作者： Yun LIU Jin SHAO Qingdao University of Science and Technology China

Diversity,relevance and ambiguity of human behavior have caused great difficulties for behavior *** paper proposes an improved fcm clustering algorithm for the problem that when clustering and modeling of behavioral characteristic descriptors in behavior recognition,the traditional fcm algorithm can not determine the number of *** algorithm is based on the indexes of inter-cluster compactness and the separation of clusters to determine the initial cluster *** model behavioral descriptors that have been clustered to reach the purpose of improving behavior recognition *** experimental results show that: the improved algorithm can classify and model behavioral descriptors better and improve the recognition accuracy.

关键词： fcm clustering algorithm Number of clusters Cluster validity index Behavior recognition

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Research on Module Diviosion Method for Product Based on fcm clustering algorithm

Research on Module Diviosion Method for Product Based on FCM...

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2011 2nd Asia-Pacific Conference on Wearable Computing Systems

作者： Yang Yanan Miao Jinghong Wang Zhongwei School of Information and Communication Engineering Tianjin Polytechnic University

From the analysis of the product of configuration values that are comprehensive consideration, the customer demand and customize its relevance and principles according to relevant factors and the establishment of a module of spare parts into a matrix with fcm clustering a module are divided on the delimitation of entropy, and to division of schemes modules on preferred.

关键词： module division production configuration correlation matrix fcm clustering algorithm normalized partition entropy.

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Long-term creep life prediction of P91 steel using domain knowledge and back propagation artificial neural network

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MATERIALS AT HIGH TEMPERATURES 2025年第1期42卷 14-24页

作者： Song, Chaolu Liu, Xinbao Zhu, Lin Fan, Ping Ren, Siyu Zhang, Kai Chen, Jie Chen, Hongtao Northwest Univ Sch Chem Engn Xian Peoples R China Northwest Univ Sch Informat Sci & Technol Xian Peoples R China

In this work, the long-term creep life of P91 steels with the limited experiment data was predicted using the domain knowledge and back propagation artificial neural networks (BP-ANN). Based on the traditional creep life models of Larson-Miller (L-M) parameter and theta projection, the domain knowledge was incorporated to expand the creep dataset. Then, the fuzzy C-means (fcm) clustering algorithm was introduced for training data collection. Consequently, the creep life prediction was conducted with the corresponding dataset and BP-ANN. The obtained results demonstrated that in contrast to the conventional prediction models of creep life, the prediction accuracy for long-term creep life of P91 steels can be improved significantly by the present model. For instance, the predicted creep lives 200,000 h exhibit the average errors of 6.0%. In addition, the present study offers a convenient tool to solve the issue of limited experiment data, particularly the long-term creep of P91 steels.

关键词： Creep life prediction small samples domain knowledge BP artificial neural networks fcm clustering algorithm

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