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A Self-Adaptive FcM for the Optimal fuzzy Weighting Exponent

INTERNATIONAL JOURNAL OF cOMPUTATIONAL INTELLIGENcE AND APPLIcATIONS 2019年第2期18卷

作者： Ren, Min Wang, Zhihao Jiang, Jirong Shandong Univ Finance & Econ Sch Math & Quantitat Econ Jinan Shandong Peoples R China Shandong Univ Finance & Econ Sch Management & Engn Jinan Shandong Peoples R China

fuzzy weighting exponent m is an important parameter of fuzzy c-means (FcM), closely related to the performance of the algorithm. First, an improved fuzzy correlation degree was put forward to measure the relevance between the clusters, based on which a new cluster validity function was defined to evaluate the quality of the fuzzy partition. Then a self-adaptive FcM for the optimal value of m was proposed with the aid of the global search ability of improved particle swarm algorithm to find out both the final clustering centroids and the optimal value of fuzzy weighting exponent automatically. The improved particle swarm algorithm updated the speed and the position based on dynamic inertia weight and learning factors, and introduced mutation of genetic algorithm to keep the diversity of the particles, preventing premature convergence. The experimental results showed that the proposed algorithm automatically calculated the optimal value of m and meanwhile achieved better clustering results.

关键词： fuzzy weighting exponent fuzzy c-means algorithm cluster validity function particle swarm optimization

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The fuzzy clustering combined an improved artificial bee colony algorithm with new rank fitness selection

The fuzzy clustering combined an improved artificial bee col...

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International conference on Software Intelligence Technologies and Applications & International conference on Frontiers of Internet of Things 2014

作者： He Yunbin Xiao Yupeng Wan Jing Li Song School of Computer Science and Technology Harbin University of Science and Technology China

The traditional fuzzy c-means clustering algorithm is easy to trap in local optimums as its sensitive selection of the initial cluster centers. For overcoming this disadvantage, this paper presents a fuzzy c-means algorithm combined an improved artificial bee colony algorithm with the strategy of rank fitness selection. The strategy is aimed to increase the selection probability of the individual with better fitness. The proposed algorithm combines the advantages of the high efficiency of fuzzy c-means algorithm and the global search ability of the artificial bee colony algorithm. The experiment and analysis results demonstrate that the algorithm can solve the optimization problem of the initial cluster centers with high robustness and better quality of clustering.

关键词： artificial bee colony algorithm clustering analysis fuzzy c-means algorithm artificial bee colony algorithm fuzzy health cluster Analysis algorithms Rank of data

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An Evolutionary clustering algorithm Based on Adaptive fuzzy Weighted Sum Validity Function

An Evolutionary Clustering Algorithm Based on Adaptive Fuzzy...

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The Third International Joint conference on computational Science and Optimization(第三届计算科学与优化国际大会 cSO 2010)

作者： Hongbin Dong Wei Hou Guisheng Yin School of Computer Science and Technology Harbin Engineering University Harbin 150001 China

In this paper, we propose a novel objective function called the adaptive fuzzy Weighted Sum Validity Function (FWSVF), which is a merged weight of the several fuzzy cluster validity functions, including XB, PE, Pc and PBMF. The improved validity function is more efficient than others. Furthermore, we present a Mixed Strategy Evolutionary clustering algorithm based adaptive validity function (AMSEcA), which is merged from Evolutionary algorithm along with Mixed Strategy and fuzzy c-means algorithm. Moreover, in the experiments, we show the effectiveness of AMSEcA, AMSEcA could find the proper number of clusters automatically as well as appropriate partitions of the data set and avoid local optima.

关键词： fuzzy c-means algorithm evolutionary algorithm the adaptive fuzzy weighted sum validity function evolutionary clustering algorithm

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A new feature-weighted relative entropy clustering algorithm 23

A new feature-weighted relative entropy clustering algorithm

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Proceedings of the 2023 6th International conference on Artificial Intelligence and Pattern Recognition

作者： Yifan Hu Jing Li Bin Jia School of Communication and Information Engineering Xi 'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

The fuzzy c-means (FcM) algorithm is one of the most widely used algorithms in unsupervised pattern recognition learning. However, real-world data is more complex and there may be some irrelevant features in the data that affect the final clustering results of FcM. The weighted clustering algorithm increases the importance of relevant features in the data by assigning different weights to features of different dimensions, and at the same time weakens the influence of irrelevant features on the clustering results. However, both the weighted clustering algorithm and the FcM algorithm will have classification errors as the observation noise increases. As a distance measure between two distributions, relative entropy is added to the objective function as a regularization function, which can minimize the distance within the cluster and maximize the difference between clusters. Therefore, this paper proposes a new feature-weighted relative entropy clustering algorithm (REFcM_EW). The REFcM_EW algorithm combines feature weight and relative entropy, which not only enhances the importance of relevant features in the data but also has better noise detection capability. Experimental results show that REFcM_EW has a good effect on the strip data.

关键词： fuzzy c-means algorithm feature weight relative entropy

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Weapon Equipment Project Interim Evaluation based on fuzzy Theory

Weapon Equipment Project Interim Evaluation based on Fuzzy T...

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2011 3rd IEEE International conference on Information Management and Engineering(IcIME 2011)

作者： chen Xiaowei,Wang Xinzheng,Song Guibao Naval Aeronautical and Astronautical University,Yantai,china,264001

Weapon equipment project interim evaluation index system and evaluation model are established in the *** index system is established from five aspects:development,organizational management,capability,prospective benefit and *** evaluation model is established by fuzzy comprehensive evaluation and fuzzy c-means *** this model,the original data of qualitative and quantitative indexes are transformed into fuzzy vectors in pretreatment;the indexes weight vectors are determined by AHP;comprehensive evaluation results are calculated by multilevel fuzzy comprehensive evaluation;then,fuzzy c-means algorithm is proposed to analyse evaluation results,which could avoid the disadvantage of information loss in the maximum membership degree method and information annihilation in weighted average *** effectiveness and practicability of evaluation index system and model are verified by the results of a case study.

关键词： weapon equipment project project interim evaluation index system fuzzy comprehensive evaluation fuzzy c-means algorithm

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An efficient fault diagnosis strategy based on SVDD and fuzzy clustering for ground-based electronic equipment

An efficient fault diagnosis strategy based on SVDD and fuzz...

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IEEE International conference on Signal Processing, communications and computing (IcSPcc)

作者： cheng Wang Huahui Yang chen Meng Army Engineering University Shijiazhuang China

ISBN: (纸本)9781728117096

In this paper, an efficient fault detection approach which employs the Support Vector Data Description (SVDD) and fuzzy c-means algorithm (FcM) is proposed for ground-based electronic equipment. Firstly, the FcM method is applied to fault pattern mining in which the prior knowledge of equipment faults is difficult to be known. Then SVDD model is trained with different faults data independently for multi-classification. This fault diagnosis strategy can be used in health condition monitoring for ground-based electronic equipment. The experimental results verify its effectiveness in fault diagnosis with high accuracy and real-time performance.

关键词： Fault diagnosis Support vector domain description fuzzy c-means algorithm condition monitoring Electronic equipment Fault diagnosis electronic equipment condition monitoring support vector data description

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An Improved Teaching-Learning-Based Optimization With Adaptive Moment Estimation for Global Optimization 23

An Improved Teaching-Learning-Based Optimization With Adapti...

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Proceedings of the 7th International conference on algorithms, computing and Systems

作者： Xiaokui Wu Zhurong Zhou Duowan Wu College of Computer & Information Science Southwest University China Department of Information Service Industry Guangzhou Industry & Trade Technician College China

ISBN: (纸本)9798400709098

The Teaching and Learning Optimization algorithm (TLBO) simulates the two main stages of traditional classroom teaching. It is divided into the "teaching stage" and the "learning stage". However, the traditional teaching process is a relatively complex process, and the simulation process of this algorithm is relatively simple. Therefore, this study introduces two new educational concepts and educational methods, "group teaching" and "adaptive learning", into the simulation process of traditional classroom teaching, and proposed an improved teaching-based optimization algorithm (TLBO-Adam) based on adaptive moment estimation. This study introduces group teaching in the "teaching stage" and uses fuzzy c-means clustering (FcM) algorithm in the field of machine learning, and introduces Adaptive learning in the "learning stage" and uses an adaptive moment estimation algorithm with the characteristics of a goal-oriented mechanism to solve the problem of premature convergence and slow convergence of the TLBO algorithm. Finally, through designing comparative experiments, experimental results show that the improved algorithm TLBO-Adam is effective in 9 test functions and can find the global optimal solution quickly and efficiently compared with other swarm intelligence algorithms.

关键词： Adaptive Moment Estimation fuzzy c-means algorithm Globally Optimal Solution Teaching–learning-based optimization

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Application of Teaching Example Based on clustering algorithm

Application of Teaching Example Based on Clustering Algorith...

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2018 International conference on Management,Economics,Education and Social Sciences(MEESS 2018)

作者： Xiang Li Xin Su Miao Di Muzi Zhuge School of Electronic Engineering Tianjin University of Technology and Education Chengxi Electric Power Supply Branch Tianjin Electric Power Co.Ltd

ISBN: (纸本)9781510870994

In this paper,K-means and fuzzy c-means clustering algorithms are used as typical algorithms for data mining *** addition,it uses electricity monthly electricity load data for a certain region in domestic as the testing *** the MATLAB environment,the clustering clusters and different clustering centers for different power consumption behaviors of power consumes are *** analyzing the clustering clusters and clustering centers,the clusters obtained by different algorithms can be roughly divided into three categories,and the power consumers' electricity habits in this area can also be divided into three *** paper uses the approach to improve students' interest in learning,expand students' practical applications in clustering algorithms,and cultivate students' creative creativity.

关键词： Data mining K-means algorithm fuzzy c-means algorithm MATLAB power consumer's behavior

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Abnormal Wind Power Data Identification Based on the Improved FcM algorithm and considering the Influence of Wind Speed

Abnormal Wind Power Data Identification Based on the Improve...

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IEEE International conference on Power and Energy Systems (IcPES)

作者： chen Fang Zhiyu Zhang Xiangjing Su Ping Zeng Shuxin Tian Grid Technology Center State Grid Shanghai Electric Power Research Institute China College of Electrical Engineering Shanghai University of Electric Power Shanghai China

ISBN: (数字)9781728126586

ISBN: (纸本)9781728126593

In the big data era, the planning, operation and maintenance of power systems are increasingly dependent on the support of various power data. Wind turbine operating data is an important data set considering the increasing installation capacity worldwide. However, due to factors such as unscheduled shutdown, load shedding and communication noise, a great variety and amount of abnormal data may be present in wind power data, which harms the economic and safe operation of wind turbines. In some cases, abnormal operating status even cannot be detected in time due to the disruption of abnormal data, causing serious accidents. Therefore, it is necessary to identify the abnormal wind power data from massive measurements and ensure the availability of accurate and effective data. In this paper, an abnormal wind power data identification strategy is proposed by the improved fuzzy c-means (FcM) algorithm and considering the influence of wind speed. Specifically, the feasible domain matrix is employed to identify abnormal data while the mean comparison method is utilized for data correction. The feasibility and effectiveness of the proposed abnormal wind power data identification and correction strategies in this study is tested by detailed simulations on the East-china Sea offshore wind power ScADA database.

关键词： abnormal data identification fuzzy c-means algorithm wind power and data correction Wind energy Wind Energy conversion Systems noise(communication) data correction wind speed Safe handling

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Research on Leak Location Method of Water Supply Network based on Deep Neural Network Model

Research on Leak Location Method of Water Supply Network bas...

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第四届材料科学应用与能源材料国际研讨会

作者： Xiaoxuan Wu chen Zhang School of Artificial Intelligence and Big Data Hefei University Anhui Engineering Lab of Big Data Technology Application for Urban Infrastructure Hefei University

The water supply network is one of the important infrastructure in urban construction. It has strong theoretical and practical significance to realize the real-time monitoring and leak location of the water supply network. In this paper, based on the similarity of water supply network node pressure, fuzzy c-means clustering algorithm is used to realize the selection of finite monitoring points. On this basis, a depth neural network model is constructed according to the pressure changes of the monitoring points before and after the leakage of the water supply network, so as to locate the leakage points. In the experimental part, hydraulics simulation was conducted by using EPANETH pipe network adjustment software according to the layout structure of water supply network, and the pressure of all nodes was obtained. A deep neural network model was established by Keras in Tensorflow framework. After model training and testing, the training error was controlled within the effective range of 5%.Finally, the model is applied to the actual leakage problem of underground water supply network in Langxi county of Xuancheng city, and the accurate location of the leakage point is realized. The experiment proves the feasibility and accuracy of the method proposed in this paper.

关键词： clustering Ensemble Deep Neural Network fuzzy c-means algorithm Leak Location Water Supply Network

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