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Design of fault diagnosis algorithm for electric fan based on LSSVM and Kd-Tree

APPLIED INTELLIGENcE 2021年第2期51卷 804-818页

作者： Hu, Kongzhi Jiang, Ming Zhang, Haifeng cao, Sheng Guo, Ziyi Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China

currently, the complexity of mechanical equipment is increasing rapidly together with the poor working environment. If a fault occurs, how to find the fault in time becomes a poser. Motivated by this existing problem, based on the analysis of the fault characteristics of electric fans, a fault diagnosis algorithm model based on Least Square Support Vector Machine (LSSVM) and Kd-Tree was proposed. This algorithm was based on the LSSVM optimized by the cuckoo Search (cS). This paper used the "one-to-many" principle and the sigma threshold method to introduce k-Nearest Neighbor (kNN) which was implemented by Kd-Tree as a secondary classifier to optimize the model. In data preprocessing, the data based on time series was first processed by Empirical Mode Decomposition (EMD) and the energy ratios were calculated, and the the above results were degraded by Principal component Analysis (PcA) and normalized. On top of that, in case of the uncertain fault types, the fuzzy c-means clustering algorithm (FcM) optimized by Particle Swarm Optimization (PSO) was proposed to provide a priori knowledge for the model. In this paper, the algorithm model, FcM and other parts were verified to prove that the performance and generality of the algorithm were better than those of general classification algorithms, and relevant experiments were conducted for different data processing methods to expand the universality of the algorithm.

关键词： Least Square Support Vector Machine Kd-Tree Data preprocessing fuzzy c-means clustering algorithm Model optimization

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A new validity function of FcM clustering algorithm based on intra-class compactness and inter-class separation

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JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2021年第6期40卷 12411-12432页

作者： Wang, H. Y. Wang, J. S. Zhu, L. F. Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China

fuzzy c-means (FcM) clustering algorithm is a widely used method in data mining. However, there is a big limitation that the predefined number of clustering must be given. So it is very important to find an optimal number of clusters. Therefore, a new validity function of FcM clustering algorithm is proposed to verify the validity of the clustering results. This function is defined based on the intra-class compactness and inter-class separation from the fuzzy membership matrix, the data similarity between classes and the geometric structure of the data set, whose minimum value represents the optimal clustering partition result. The proposed clustering validity function and seven traditional clustering validity functions are experimentally verified on four artificial data sets and six UcI data sets. The simulation results show that the proposed validity function can obtain the optimal clustering number of the data set more accurately, and can still find the more accurate clustering number under the condition of changing the fuzzy weighted index, which has strong adaptability and robustness.

关键词： fuzzy c-means clustering algorithm clustering validity function membership matrix intra-class compactness inter-class separation

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LMI Robust fuzzy c-means control for Nonlinear Systems

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JOURNAL OF cONTROL AUTOMATION AND ELEcTRIcAL SYSTEMS 2021年第4期32卷 809-814页

作者： chen, Tim chen, c. Y. J. Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City Vietnam King Abdulaziz Univ Fac Engn Jeddah 21589 Saudi Arabia

This paper addressed the robust fuzzy c-means design for a class of clustering algorithm that are robust against both the plant parameter perturbations with nonlinearity and controller gain variations. Based on the description of Takagi-Sugeno (TS) fuzzy model, the stability and control of nonlinear systems are studied. The recently proposed integral inequality is selected based on the free weight matrix, and the minimum conservative stability criterion is given in the form of linear matrix inequality (LMI). Assuming that the controller and the system have the same premise, this method does not require the number and membership function rules. In addition, the improved control is used as the stability criterion of the closed-loop TS fuzzy system obtained from LMI in large-scale nonlinear systems, and is reorganized for machine learning. The novelty of this paper is to develop a simplified and robust controller design for a class of nonlinear perturbed systems. Moreover, the proposed control process was also ensured by the control criterion derived from the energy function for the stability of the nonlinear system. Finally, a simulation is given and demonstrated the feasibility of the practical application motivated by certain concrete-real problem in vibrated structures.

关键词： LMI fuzzy criterion TS fuzzy models fuzzy c-means clustering algorithm Artificial intelligence fuzzy control Stability

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Kernel-based fuzzy c-means clustering Based on Fruit Fly Optimization algorithm

Kernel-based Fuzzy C-means Clustering Based on Fruit Fly Opt...

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IEEE International conference on Grey Systems and Intelligent Services (GSIS)

作者： Wang, Qiuping Zhang, Yiran Xiao, Yanting Li, Jidong Xian Univ Technol Sch Sci Xian Shaanxi Peoples R China Southeast Univ Coll Civil Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509066681

fuzzy clustering has emerged as an important tool for discovering the structure of data. Kernel based clustering has emerged as an interesting and quite visible alternative in fuzzy clustering. Aimed at the problems of both a local optimum and depending on initialization strongly in the fuzzy c-means clustering algorithm (FcM), a method of kernel-based fuzzy c-means clustering based on fruit fly algorithms (FOAKFcM) is proposed in this paper. In this algorithm, the fruit fly algorithm is used to optimize the initial clustering center firstly, kernel-based fuzzy c-means clustering algorithm (KFcM) is used to classify data. At the same time we reference classification evaluation index to choose the fuzziness parameter in adaptive way. The clustering performance of FcM algorithm, KFcM algorithm, and the proposed algorithm is testified by test datasets. FcM algorithm and FOAKFcM are used for power load characteristic data classification, respectively. Experiment results show that FOAKFcM algorithm proposed overcomes FcM's defects efficiently and improves the clustering performance greatly.

关键词： clustering analysis Fruit fly optimization algorithm fuzzy c-means clustering algorithm KFcM fuzzy degree of parameters

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Soil image segmentation based on fuzzy clustering OTSU

Soil image segmentation based on fuzzy clustering OTSU

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International conference on Image Processing and Intelligent control (IPIc)

作者： Shen, Guochao Li, Jiaojie cheng, Quchao Shanghai Dianji Univ Sch Elect Engn Pudong New Area 300 Shuihua Rd Shanghai Peoples R China

ISBN: (数字)9781510647251

ISBN: (纸本)9781510647251;9781510647244

The composition of the soil is very important, therefore, it is necessary to separate soil and other components from soil images in order to facilitate the study of soil components. This paper mainly studies the realization method of soil image segmentation, especially the traditional maximal inter-class variance method in global threshold method. On this basis, the fuzzy c-means clustering algorithm is combined with fuzzy theory to optimize the algorithm. By comparing the experimental results, it is proved that the fuzzy c-means clustering algorithm based on the maximum inter-class segmentation method can achieve the segmentation of objects and backgrounds, and meet the requirements of image segmentation.

关键词： The threshold value Maximum inter-class variance method fuzzy c-means clustering algorithm Image segmentation

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A new robust fuzzy clustering framework considering different data weights in different clusters

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EXPERT SYSTEMS WITH APPLIcATIONS 2022年 206卷

作者： Wu, Ziheng Wang, Bing Li, cong AnHui Univ Technol Anhui Prov Key Lab Special & Heavy Load Robot Maanshan 243032 Peoples R China AnHui Univ Technol Sch Elect & Informat Engn Maanshan 243032 Peoples R China

In conventional fuzzy c-means clustering algorithms, each data and each feature are treated equally, the clustering performance is sensitive to the noise points;in existing weighting clustering algorithms, few studies have focus on data weighting and feature weighting simultaneously, besides, the same data in different clusters is treated equally. To address this issue, in this paper, taking the different data weights in different clusters and the different feature weights in different clusters into consideration, we present a new robust fuzzy c-means clustering framework. For the first time, we propose a whole new idea that the same data in different clusters should have different importance, the different data in a cluster should have different importance, as well;By the new data weighting method, the proposed clustering algorithm can weaken the impact of noise points on the formation of each clustering center, which could enhance the robustness of clustering;to stimulate more data and more features to take part in the process of clustering and to avoid overfitting, we add l(2)-norm regularization of the data weights and l(2)-norm regularization of the feature weights to the objective function. Then, based on the presented objective function, we get the scientific update rules of the different data weights in different clusters, the different feature weights in different clusters, the membership degrees, and the cluster centers, during each iteration. To assess the performance of the new fuzzy c-means framework, experimental verifications on synthetic dataset and real-world datasets are conducted, experimental results have shown that the new algorithm can achieve better clustering performances in comparison to other related clustering methods.

关键词： fuzzy c-means clustering algorithm Data weights Feature weights l(2)-Norm regularization

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Design and implementation of intelligent accounting data analysis platform based on industrial cloud computing

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EURASIP JOURNAL ON WIRELESS cOMMUNIcATIONS AND NETWORKING 2020年第1期2020卷 1-8页

作者： Ting, Wang Liu, Yang Shandong Vocat Coll Light Ind Dept Business Adm Zibo 255300 Peoples R China Qingdao Univ Sch Business Qingdao 266100 Peoples R China

In order to improve the ability of accounting data analysis and statistics, a method of constructing modern accounting data analysis platform based on industrial cloud computing is proposed. The time series model of modern accounting data analysis is constructed by using block bit sequence analysis method, and the association rule characteristic quantity of accounting data is extracted. combined with cloud computing technology, the modern accounting data analysis platform is constructed, and the fuzzy c-means clustering algorithm is used to realize the clustering of modern accounting data, which can improve the ability of parallel computing and statistical analysis of accounting data. The simulation results show that the intelligent data analysis platform designed makes the statistical analysis ability of accounting data better and the parallel computing efficiency higher.

关键词： Industrial cloud computing Intelligent accounting data analysis fuzzy c-means clustering algorithm Feature extraction

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Predictive model of energy consumption for office building by using improved GWO-BP

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ENERGY REPORTS 2020年 6卷 620-627页

作者： Tian Ying Yu Junqi Zhao Anjun Xian Univ Architecture & Technol Sch Bldg Equipment Sci & Engn Xian 710055 Shaanxi Peoples R China Xian Univ Architecture & Technol Shaanxi Inst New Urbanizat & Human Settlements Xian 710055 Shaanxi Peoples R China

Building energy data analysis is a major branch of smart city development research. The usual back propagation neural network model for building energy prediction has problems of unclear physical significance, poor data generalization and low fitting accuracy. Therefore, a composite prediction model of building power consumption based on FcM-GWO-BP neural network was proposed. According to the similar statistical distribution characteristics of data, the fuzzy c-means clustering algorithm (FcM) was used to cluster the historical power consumption data. BP neural network prediction model was established for different categories to reduce the impact of relevant noise in the sample data on the modeling accuracy. Then, according to the train and test data sets of each category, the corresponding grey wolf algorithm was established to optimize the error back propagation neural network prediction model (GWO-BP). The experimental results showed that compared with the sample prediction accuracy index root mean square percentage error (RMSPE), the GWO-BP neural network after FcM clustering was reduced by about 0.225 compared with the BP model, and was reduced by about 0.135 compared with the GWO-BP model, so its prediction accuracy was improved by 75% at most. Respectively, the mean absolute percentage error (MAPE) was reduced by 14.41% and 6.48%. It can be seen that this model has strong generalization ability, better prediction accuracy and reliability, and absolutely can meet the needs of practical engineering. (c) 2020 The Authors. Published by Elsevier Ltd.

关键词： Building electrical load prediction BP neural network fuzzy c-means clustering algorithm Grey wolf optimizer algorithm

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Forecasting carbon price using empirical wavelet transform and gated recurrent unit neural network

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cARBON MANAGEMENT 2020年第1期11卷 25-37页

作者： Liu, Hui Shen, Lei North China Elect Power Univ Dept Econ & Management Baoding 071000 Peoples R China State Grid Anhui Elect Power Co Ltd Econ & Technol Res Inst Hefei Anhui Peoples R China

carbon price, to a certain extent, reflects the intensity of a national emission reduction target, whereas carbon price forecasting is the basis for improving crisis management competence and strengthening market enthusiasm. This paper advances a novel hybrid carbon price forecasting methodology consisting of the empirical wavelet transform (EWT) and the gated recurrent unit (GRU) neural network. First, the carbon price data is decomposed through the EWT approach into the more stable and regular sub-components. These sub-components are divided into trend, low-frequency and high-frequency component using the fuzzy c-means clustering algorithm. Next, the lag order of different classes of components is determined as the input variables of the GRU model by the partial auto-correlation function method. Then, all values of each component predicted by the GRU method are aggregated to produce a final combined prediction result for the original carbon price. Finally, the EWT-GRU model is compared with the individual Autoregressive Integrated Moving Average (ARIMA), Back Propagation Neural Network (BPNN), GRU and EWT-BPNN models. The simulation results demonstrate that the proposed EWT-GRU combined forecasting model is superior to other models in terms of prediction effect, prediction accuracy, etc. They also confirm the validity and accuracy of the EWT-GRU model in carbon price prediction and show it deserves popularization.

关键词： carbon price forecasting empirical wavelet transform gated recurrent unit neural network fuzzy c-means clustering algorithm

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State Recognition of Around-city Expressway Based on Improved FcM algorithm 2021

State Recognition of Around-city Expressway Based on Improve...

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2021 The 9th International conference on Information Technology: IoT and Smart city

作者： Zhongyang Tao congyong cao Xinmiao Yao Zhen Gong School of Automation Nanjing University of Science and Technology China

ISBN: (纸本)9781450384971

With the goal of accurately identifying the traffic state of the expressway around the city, the traffic flow parameters of the expressway around the city in large cities in my country are used for research, and the monitoring data of the expressway section is excavated and processed, so as to obtain the traffic volume, time average speed and time of a specific road section. Occupancy rate data. Improved the fuzzy c-means clustering (FcM) algorithm, based on the data density (DKc) value to optimize the FcM clustering center, after obtaining the randomly generated clustering center, use the DKc value to compare large cluster centers are replaced, which makes the cluster centers more valuable. Taking a typical section of Suzhou Ring Expressway as an example, the case analysis shows that each cluster basically conforms to the normal distribution, and the root mean square error of the fitting is below 0.0497, which is very consistent with the highway conditions in the daily state.

关键词： fuzzy c-means clustering algorithm Traffic state recognition Around-city expressway

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