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fuzzy identification of nonlinear dynamic system based on selection of important input variables

Journal of Systems Engineering and Electronics 2022年第3期33卷 737-747页

作者： LYU Jinfeng LIU Fucai REN Yaxue Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan UniversityQinhuangdao 066004China School of Mathematics and Information Science and Technology Hebei Normal University of Science and TechnologyQinhuangdao 066004China

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 ***, 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.

关键词： Takagi-Sugeno(T-S)fuzzy modeling input variable selection(IVS) fuzzy identification fuzzy c-means clustering algorithm

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fuzzy c-means robust algorithm for nonlinear systems

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SOFT cOMPUTING 2021年第11期25卷 7297-7305页

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

This paper addresses the criterion of the robust controller design for the solution of a number of fuzzy c-means clustering algorithms, which are robust to plant parameter disturbances and controller gain variations. The control and stability problems in the present nonlinear systems are studied based on a Takagi-Sugeno (T-S) fuzzy model. A lately and important proposed integral inequality is considered and selected according to the method of the free weight matrix, with these comparatively flexible stability criteria which are determined in the numerical form of linear matrix inequalities (LMIs). Under the condition of the premise in which the controller and the control system partake the same rules, the method does not inquire the same number of membership functions and mathematical rules. In addition, the improved control is used for large-scale nonlinear systems, where the stability criterion of the closed T-S fuzzy system is obtained through LMI and rearranged through the membership function for machine learning . The close-loop controller criteria are derived by using the Lyapunov energy functions to guarantee the stability of the system . Eventually, an instance is presented to reveal the efficacy of evolution.

关键词： LMI fuzzy criterion T– S fuzzy models Model-free sliding mode fuzzy c-means clustering algorithm fuzzy control Stability

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Educational data mining for students' performance based on fuzzy c-means clustering

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JOURNAL OF ENGINEERING-JOE 2019年第11期2019卷 8245-8250页

作者： Li, Yu Gou, Jin Fan, Zongwen Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Fujian Peoples R China Univ Newcastle Sch Elect Engn & Comp Callaghan NSW 2308 Australia

Education greatly aids in the process of students' growth;therefore, education institutions try to provide high-quality education to their students. A possible remedy to provide high-quality education is by discovering knowledge from educational data. However, accurately evaluating students' performance is very challenging due to different sources and structures of educational data. In addition, different teaching strategies are required because students' learning ability are different. One way to discover the hidden knowledge from educational data is the use of clustering algorithms, which are capable of mining interesting patterns from educational data. Thus, this study presents a fuzzy c-means clustering algorithm using 2D and 3D clustering to evaluate students' performance based on their examination results (the examination grades from college of computer Science and Technology, Huaqiao University for students enrolled in year 2014). Based on the experimental results from 2D and 3D clustering for evaluating students' performance, the educators can better understand the students' performance so as to build a pedagogical basis for decisions. Students can also receive some recommendations from the mining results about their performance.

关键词： teaching data mining educational institutions pattern clustering educational administrative data processing educational data mining education institutions high-quality education student performance educational data fuzzy c-means clustering algorithm teaching strategies 3D clustering 2D clustering

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Three-dimensional gravity inversion based on optimization processing from edge detection

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Geodesy and Geodynamics 2022年第5期13卷 503-524页

作者： Sheng Liu Shuanggen Jin Qiang chen Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China University of Chinese Academy of Sciences Beijing 100049China Shanghai Astronomical Observatory Chinese Academy of SciencesShanghai 200030China School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and TechnologyNanjing 210044China

Gravity inversion requires much computation,and inversion results are often *** first problem is often due to the large number of grid *** detection method,i.e.,tilt angle method of analytical signal amplitude(TAS),helps to identify the boundaries of underground geological anomalies at different depths,which can be used to optimize the grid and reduce the number of grid *** requirement of smooth inversion is that the boundaries of the meshing area should be continuous rather than *** this paper,the optimized meshing strategy is improved,and the optimized meshing region obtained by the TAS is changed to a regular region to facilitate the smooth *** the second problem,certain constraints can be used to improve the accuracy of *** results of analytic signal amplitude(ASA)are used to delineate the central distribution of geological *** propose a new method using the results of ASA to perform local constraints to reduce the non-uniqueness of *** guided fuzzy c-means(FcM)clustering algorithm combined with priori petrophysical information is also used to reduce the non-uniqueness of gravity *** Open Acc technology is carried out to speed up the computation for parallelizing the serial program on *** general,the TAS is used to reduce the number of grid *** local weighting and priori petrophysical constraint are used in conjunction with the FcM algorithm during the inversion,which improves the accuracy of *** inversion is accelerated by the Open Acc technology on *** proposed method is validated using synthetic data,and the results show that the efficiency and accuracy of gravity inversion are greatly improved by using the proposed method.

关键词： Gravity inversion Locally weighted constraint Petrophysical constrain fuzzy c-means clustering algorithm Open Acc technology

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Decentralized fuzzy c-means robust algorithm for continuous systems

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AIRcRAFT ENGINEERING AND AEROSPAcE TEcHNOLOGY 2020年第2期92卷 222-228页

作者： chen, Tim chen, J. c. Y. Ton Duc Thang Univ Fac Informat Technol AI LAB Ho Chi Minh City Vietnam King Saud Univ Fac Engn Riyadh Saudi Arabia

Purpose This paper aims to address the robust controller design problem for a class of fuzzy c-means clustering algorithm that is robust against both the plant parameter perturbations and controller gain variations. Based on Takagi-Sugeno (T-S) fuzzy model description, the stability and control problems of nonlinear systems are studied. Design/methodology/approach A recently proposed integral inequality is selected based on the free-weight matrix, and the less conservative stability criterion is given in the form of linear matrix inequalities (LMIs). Findings Under the premise that the controller and the system share the same, the method does not require the number of membership functions and rules. Originality/value The closed-loop controller criterion is derived by energy functions to guarantee the stability of systems. Finally, an example is given to demonstrate the results.

关键词： fuzzy c-means clustering algorithm Model-free sliding mode TS fuzzy models LMI fuzzy criterion fuzzy control Stability

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Identification of Priority Areas of Improvement for Small Passenger car Segment in Indian Market

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VISION-THE JOURNAL OF BUSINESS PERSPEcTIVE 2023年第2期27卷 225-242页

作者： Bera, Reema Maitra, Bhargab Indian Inst Technol Kharagpur Civil Engn Dept Kharagpur West Bengal India Indian Inst Technol Kharagpur Dept Kharagpur 721302 West Bengal India

The present study reports an investigation on identification of priority areas of improvement for small passenger car segment in the Indian market. A total of 750 responses on importance and satisfaction rating of 16 attributes in a 7-point Likert-type ordinal rating scale were collected from small car owners in Delhi using a paper and pencil instrument. Revised importance-performance analysis with fuzzy c-means clustering algorithm was used to identify the priority areas of improvement in small passenger cars. The analysis technique effectively identified the factor structure of consumer satisfaction and derived various management schemes for small passenger cars by analysing consumers' importance and satisfaction data. The priority areas of improvement were obtained by comparing the factor structure with the management scheme. The results indicate that safety, security and advanced vehicle technology options are the priority areas of improvement for small passenger cars. The study identified that the consumers are satisfied with the performance of purchase price, driving range, annual fuel cost, annual maintenance cost, top speed, seating comfort, appearance/style, emission, acceleration time and resale value in small cars, indicating the need to retain these attributes at their present levels to maintain competitive advantage. The engine power is identified under 'possible overkill', which indicates an opportunity for automobile manufacturers to possibly reduce the engine power to save some resources and reallocate the same for improvement of priority attributes. The findings of the present study provide valuable insight into the product specifications that the automobile manufacturers should focus on, to make new-generation small cars more attractive to Indian consumers.

关键词： Automobile Manufacturer consumer Satisfaction Revised Importance-Performance Analysis fuzzy c-means clustering algorithm Small Passenger car

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A fuzzy Portfolio Model With cardinality constraints Based on Differential Evolution algorithms

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INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING 2024年第1期20卷 1页

作者： He, JianDong Jiaxing Vocat & Tech Coll Jiaxing Peoples R China

Uncertain information in the securities market exhibits fuzziness. In this article, expected returns and liquidity are considered as trapezoidal fuzzy numbers. The possibility mean and mean absolute deviation of expected returns represent the returns and risks of securities assets, while the possibility mean of expected turnover represents the liquidity of securities assets. Taking into account practical constraints such as cardinality and transaction costs, this article establishes a fuzzy portfolio model with cardinality constraints and solves it using the differential evolution algorithm. Finally, using fuzzy c -means clustering algorithm, 12 stocks are selected as empirical samples to provide numerical calculation examples. At the same time, fuzzy c -means clustering algorithm is used to cluster the stock yield data and analyse the stock data comprehensively and accurately, which provides a reference for establishing an effective portfolio.

关键词： the efficient frontier of the fuzzy portfolio model fuzzy c-means clustering algorithm liquidity transaction costs trapezoidal fuzzy numbers

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Framework for Prediction of Diabetes using FireFly Swarm Intelligence algorithm, fuzzy c Mean and SVM algorithm 5

Framework for Prediction of Diabetes using FireFly Swarm Int...

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5th International conference on Inventive computation Technologies (IcIcT)

作者： Kalpana, c. Booba, B. Vels Univ Dept CSE VISTAS Chennai Tamil Nadu India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Diabetes is a metabolic sickness that remains to be a major universal problem because it influences the health of the entire population. Over the years several researchers have attempted to build a model for predicting diabetes accurately. However, this field study remains a challenge because of the unavailability of dataset and prediction models which forces the researchers to utilize ML (Machine Learning) algorithm and Big data analytics. The primary objective of the study was to identify how ML and Big data analytics can be adopted in diabetes. The evaluation of outcomes demonstrates that the proposed ML-based technique might achieve 8.6% of accuracy. The authors performed a review of literature on the ML models and came out with the suggestion of an Automatic Intelligent method for the prediction of diabetes based on the findings. The authors explored ML approaches and proposed and developed an intelligent ML-based architecture for diabetes prediction. In this work, the authors used fuzzy mean, SVM learning models for the prediction of diabetes. A firefly algorithm optimized classifies was utilized for selecting the best features. It is proposed in the work, that an exclusive automatic intelligent optimize diabetic prediction model is built by utilizing ML approaches. The framework was created following a thorough analysis of existing prediction approaches in the literature and assessing their relevance to diabetes. The authors presented the training methods, model evaluation strategies, and the challenges allied to the prediction of diabetes also the remedies they provide by using the framework. Health professionals, students, researchers, and stakeholders working in diabetes forecast research development may benefit from the findings of the study. The proposed work achieves an accuracy of 86 percent with low error rates.

关键词： fuzzy c-means clustering algorithm diabetes diagnosis medical data mining classification firefly algorithm

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Application of artificial intelligence based on deep learning in breast cancer screening and imaging diagnosis

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NEURAL cOMPUTING & APPLIcATIONS 2021年第15期33卷 9637-9647页

作者： Wang, Yang Yang, Fuqian Zhang, Jun Wang, Huidong Yue, Xianwen Liu, Shanshan Weifang Peoples Hosp Dept Galactophore Surg Weifang 261041 Shandong Peoples R China Weifang Resp Dis Hosp Dept Radiol Weifang 261041 Shandong Peoples R China Weifang Peoples Hosp Dept Cardiol Weifang 261041 Shandong Peoples R China

In recent years, with the change of lifestyle in Europe and America, the incidence of breast cancer in chinese women is increasing. In order to find the model of breast cancer image screening and diagnosis with higher accuracy and better classification performance, this paper mainly constructs the breast cancer cT image detection model and the breast cancer screening model based on the convolution and deconvolution neural network (cDNN) through the convolution neural network (cNN). In this paper, the fuzzy c-means clustering algorithm (FcM) is used to improve and optimize the image of breast cancer, and the experimental results are analyzed. The optimized kernel fuzzy c-means clustering algorithm was tested on a common dataset to segment the region of interest more accurately. Our experiments show that the new deep learning model of this paper improves the automatic classification performance of breast cancer. In this paper, the research results of deep learning are applied to the medical field, and a new method based on cNN model for breast cancer screening and diagnosis is proposed, which provides a new idea for improving the artificial intelligence assisted medical diagnosis method.

关键词： Breast cancer screening convolutional neural network (cNN) convolutional deconvolutional neural network (cDNN) fuzzy c-means clustering algorithm

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

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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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