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Federated conference on computer Science and Information Systems (FedcSIS)

作者： Sakarya, Omer Univ Gdansk Inst Informat PL-80952 Gdansk Poland

ISBN: (纸本)9788360810668

The goal of this paper was to apply fuzzy clustering algorithm known as fuzzy c-means to color image segmentation, which is an important problem in pattern recognition and computer vision. For computational experiments, serial and parallel versions were implemented. Both were tested using various parameters and random number generator seeds. Various distance measures were used: Euclidean, Manhattan metrics and two versions of Gower coefficient similarity measure. The F and Q segmentation evaluation measures and output images were used to assess the result of color segmentation. Serial and parallel run times were compared.

关键词： fuzzy set theory image colour analysis image segmentation pattern clustering random number generation Euclidean distance measures F segmentation evaluation measures Gower coefficient similarity measure Manhattan metrics Q segmentation evaluation measures color image segmentation computer vision fuzzy c-means clustering algorithm pattern recognition random number generator seeds clustering algorithms clustering methods color Image color analysis Image segmentation Measurement image segmentation image colour analysis clustering methods clustering algorithms images pseudorandom number generation computer vision Pattern recognition fuzzy Set Theory cluster Analysis color image fuzzy segmentation Segmentation

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Multi-model direct generalised predictive control for automatic train operation system

Multi-model direct generalised predictive control for automa...

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20th ITS World congress

作者： Wen, Shuhuan Yang, Jingwei Rad, Ahmad B. Hao, Pengcheng Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China Simon Fraser Univ Sch Engn Sci Surrey BC V3T 0A3 Canada

The authors propose a novel multi-model direct generalised predictive control based on predictive function control (PFc) algorithm for automatic train operation system. The proposed method facilitates autonomous driving of a train through a given guidance trajectory. Firstly, they present a multi-model architecture based on fuzzy c-means clustering algorithm. In order to obtain the optimal number of sub-linear models, they apply Xie-Beni cluster validity index. In this regards, the multi-model set is established off-line. Secondly, the proper sub-linear model is selected as the predictive model by using switching performance index at each time slot. The control variables are calculated by direct generalised predictive controller based on PFc. The control algorithm is simple, and can reduce the on-line computation time by directly identifies the unknown parameters in the controller. It can avoid recursively solving the Diophantine equations. The calculation of compensation value becomes simple by introducing PFc. Finally, simulation results are provided to show the effectiveness of the proposed scheme.

关键词： predictive control trajectory control fuzzy set theory pattern clustering performance index time-varying systems railways rail traffic control multimodel direct generalised predictive control automatic train operation system predictive function control algorithm PFc algorithm autonomous train driving trajectory guidance multimodel architecture fuzzy c-means clustering algorithm sublinear models Xie-Beni cluster validity index sublinear model switching performance index online computation time Diophantine equations compensation value

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fuzzy PID controller Multiobjective Genetic Design 10

Fuzzy PID Controller Multiobjective Genetic Design

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10th IEEE conference on Industrial Electronics and Applications

作者： Lima, Fernanda Serra, Ginalber Univ Fed Maranhao PhD & Master Program Elect Engn Ave Portugueses 1966 BR-65080805 Sao Luis MA Brazil Fed Inst Educ Sci & Technol Dept Elect Elect BR-65030005 Sao Luis MA Brazil

ISBN: (纸本)9781479983896

A robust fuzzy control design, with time delay, based on gain and phase margins specifications for nonlinear systems, in the continuous time delay, is proposed. From input and output data of the process, a fuzzy c-means (FcM) clustering algorithm estimates the antecedent parameters and the rules number of a Takagi-Sugeno fuzzy model, whereas the least squares algorithm estimates the consequent parameters. A multiobjective genetic strategy is developed to tune the fuzzy digital controller parameters, so the gain and phase specified margins are obtained for the fuzzy control system. The fuzzy PID controller was implemented on a real time acquisition data platform, based on compactRIO (NI cRIO-9073) and LabVIEW, from National Instruments, for temperature control of a thermic process. The experimental results show the efficiency of the proposed methodology through the accuracy in the gain and phase margins of the PID control system compared to the specified ones and tracking of the reference trajectory. fuzzy PID controller also is more efficient when compared to the fuzzy delay and lead compensator.

关键词： continuous systems control system synthesis delays fuzzy control fuzzy set theory nonlinear control systems robust control three-term control compactRIO FcM clustering algorithm LabVIEW Takagi-Sugeno fuzzy model antecedent parameters continuous time delay fuzzy PID controller multiobjective genetic design fuzzy c-means clustering algorithm fuzzy delay gain margin lead compensator least squares algorithm multiobjective genetic strategy nonlinear systems phase margin proportional-integral-derivative controller robust fuzzy control design rules number temperature control thermic process clustering algorithms fuzzy control Genetics Mathematical model Robustness Takagi-Sugeno model

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Ensemble fuzzy classifiers design using weighted aggregation criteria

Ensemble fuzzy classifiers design using weighted aggregation...

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IEEE International conference on fuzzy Systems (FUZZ-IEEE)

作者： Salgado, catia M. Azevedo, carlos S. Garibaldi, Jonathan Vieira, Susana M. Univ Lisbon IDMEC Inst Super Tecn Ave Rovisco Pais P-1699 Lisbon Portugal Univ Nottingham Sch Comp Sci Nottingham NG7 2RD England

ISBN: (纸本)9781467374286

The rationale behind ensemble machine learning systems is the creation of many classifiers and the combination of their output such that the combination improves the performance of each single classifier. There are two key issues in the creation of ensemble classifiers: one is how two select and group the data samples to train the individual models and the other is how to select or combine the multiple outputs. In this work, the fuzzy c-means clustering algorithm is used to identify groups of samples with common characteristics, and each group is used to develop individual fuzzy classifiers. The final classification of the ensemble fuzzy classifier is obtained using two different output aggregation decision criteria. The two decision criteria proposed are based on the arithmetic mean of the multiple classifiers output and on the fuzzy weighted average of the output of each classifier with the distance to the corresponding cluster. The objective of this work is to see if greater predictive performance can be achieved using multiple output aggregation techniques (arithmetic mean and distance-weighted mean criteria) as compared to selection techniques based on distance metrics to select which classifier to be used. The performance of the presented approach is tested using two real world clinical databases and two benchmark datasets from the UcI benchmark repository. Results show that the distance-weighted mean performs better than the arithmetic mean criteria. Overall, no significant difference was found between the performance of aggregation and selection classifiers.

关键词： aggregation decision making fuzzy set theory learning (artificial intelligence) pattern classification UcI benchmark repository arithmetic mean criteria benchmark datasets distance metrics distance-weighted mean criteria ensemble fuzzy classifier design ensemble machine learning systems fuzzy c-means clustering algorithm fuzzy weighted average multiple output aggregation techniques output aggregation decision criteria weighted aggregation criteria clustering algorithms Data models Databases MIMIcs Mathematical model Measurement Predictive models distance metric data models Millimeter wave monolithic integrated circuits clustering algorithms Pattern recognition fuzzy Set Theory Prediction models Mathematical Model classifiers Database Weighted Average agglomeration Mimicry decision making Data sampling

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A New RBF Neural Network with GA-based fuzzy c-means clustering algorithm for SINS Fault Diagnosis

A New RBF Neural Network with GA-based Fuzzy C-Means Cluster...

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2009中国控制与决策会议

作者： Zhide Liu,Jiabin chen,chunlei Song School of Automation,Beijing Institute of Technology,Beijing 100081,china

In this paper,a new radial basis function(RBF) neural network with fuzzy c-means clustering algorithm based on genetic algorithm(GA) is proposed for the fault diagnosis of gyroscopes and accelerometers of strapdown inertial navigation system(SINS).The fuzzy c-means algorithm(FcM) tends to fall into the local *** fuzzy c-means clustering algorithm combined with GA(FGA) obtains the global optimal cluster *** is used to provide the optimal cluster centers for RBF neural network,and a second order learning algorithm is used to train the parameters and weights of RBF neural *** results show that the proposed RBF neural network with FGA quickly converges and effectively improves the diagnostic accuracy rate of SINS fault diagnosis.

关键词： Radial basis function neural network Fault diagnosis Strapdown inertial navigation system fuzzy c-means clustering algorithm Genetic algorithm

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Automatic structure identification of TSK fuzzy model for stock index forecasting

Automatic structure identification of TSK fuzzy model for st...

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IEEE International conference on fuzzy Systems

作者： Jungwon Yu Sungshin Kim Dept. of Electr. & Comput. Eng. Pusan Nat. Univ. Busan South Korea

ISBN: (纸本)9781467374293

Among financial time-series analysis tasks, stock index forecasting has been considered as one of the challenging and difficult tasks. Since an accurate stock index prediction enhances stock market returns, it is highly promising research area and has attracted particular attention. In this paper, to predict stock index with complex and nonlinear characteristics, an automatic structure identification (SI) method of TSK fuzzy model is proposed. Typically, SI procedures of fuzzy models consist of relevant input selection, fuzzy rule generation and parameter search space determination. In this study, mutual information is employed to select relevant input variables and fuzzy c-means (FcM) clustering algorithm is used to generate fuzzy if-then rules. In FcM clustering, the number of clusters should be fixed in advance. This paper uses performance criterion to determine the optimal number of clusters in FcM clustering. After deciding the optimal cluster number, fuzzy if-then rules are extracted and parameter search space boundaries are fixed. Finally, premise and consequent parameters are optimized by cooperative random learning particle swarm optimization proposed by Zhao et al. To confirm the effectiveness, the proposed TSK fuzzy modeling method and comparison methods are applied to Korea composite Stock Price Index dataset. The experimental results show that the TSK fuzzy models with the proposed SI method outperform comparison methods.

关键词： economic forecasting fuzzy set theory learning (artificial intelligence) particle swarm optimisation pattern clustering stock markets time series FcM clustering algorithm Korea composite Stock Price Index dataset TSK fuzzy model automatic SI method automatic structure identification method complex-nonlinear characteristics cooperative random learning particle swarm optimization financial time-series analysis tasks fuzzy c-means clustering algorithm fuzzy if-then rule generation fuzzy rule generation input variable selection mutual information optimal cluster number parameter search space parameter search space boundaries performance criterion stock index forecasting stock index prediction stock market return enhancement Forecasting Hidden Markov models Indexes Input variables Predictive models Silicon Yttrium structure identification

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Effective fuzzy clustering algorithm with Bayesian model and mean template for image segmentation

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IET IMAGE PROcESSING 2014年第10期8卷 571-581页

作者： Zhang, Hui Wu, Qing Ming Jonathan Zheng, Yuhui Thanh Minh Nguyen Wang, Dingcheng Nanjing Univ Informat Sci & Technol Sch Comp Software Sch Comp & Software Nanjing Jiangsu Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing Jiangsu Peoples R China

fuzzy c-means (FcMs) with spatial constraints have been considered as an effective algorithm for image segmentation. The well-known Gaussian mixture model (GMM) has also been regarded as a useful tool in several image segmentation applications. In this study, the authors propose a new algorithm to incorporate the merits of these two approaches and reveal some intrinsic relationships between them. In the authors model, the new objective function pays more attention on spatial constraints and adopts Gaussian distribution as the distance function. Thus, their model can degrade to the standard GMM as a special case. Our algorithm is fully free of the empirically pre-defined parameters that are used in traditional FcM methods to balance between robustness to noise and effectiveness of preserving the image sharpness and details. Furthermore, in their algorithm, the prior probability of an image pixel is influenced by the fuzzy memberships of pixels in its immediate neighbourhood to incorporate the local spatial information and intensity information. Finally, they utilise the mean template instead of the traditional hidden Markov random field (HMRF) model for estimation of prior probability. The mean template is considered as a spatial constraint for collecting more image spatial information. compared with HMRF, their method is simple, easy and fast to implement. The performance of their proposed algorithm, compared with state-of-the-art technologies including extensions of possibilistic fuzzy c-means (PFcM), GMM, FcM, HMRF and their hybrid models, demonstrates its improved robustness and effectiveness.

关键词： Bayes methods fuzzy set theory Gaussian distribution image segmentation pattern clustering probability image spatial information prior probability estimation HMRF model hidden Markov random field model intensity information local spatial information immediate neighbourhood fuzzy memberships image sharpness image pixel prior probability FcM methods distance function Gaussian distribution objective function Gaussian mixture model GMM spatial constraints fuzzy c-means clustering algorithm image segmentation mean template Bayesian model

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fuzzy c-means clustering algorithm with Unknown Number of clusters for Symbolic Interval Data

Fuzzy C-Means Clustering Algorithm with Unknown Number of Cl...

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Annual conference of the SIcE

作者： chuang, chen-chia Jeng, Jin-Tsong Li, chih-Wen Natl Ilan Univ Dept Elect Engn Ilan Taiwan Natl Farmosa Univ Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9784907764302

In this study, the concepts of competitive agglomeration clustering algorithm is incorporated into fuzzy c-means (FcM) clustering algorithm for symbolic interval-values data. In the proposed approach, called as IFcMwUNc clustering algorithm, the problems of the unknown clusters number and the initialization of prototypes in the FcM clustering algorithm for symbolic interval-values data are overcome and discussed. Due to the competitive agglomeration clustering algorithm possess the advantages of the hierarchical clustering algorithm and the partitional clustering algorithm, IFcMwUNc clustering algorithm can be fast converges in a few iterations regardless of the initial number of clusters. Moreover, it is also converges to the same optimal partition regardless of its initialization. Experiments results show the merits and usefulness of IFcMwUNc clustering algorithm for the symbolic interval-values data.

关键词： Symbolic interval-values data competitive agglomeration clustering algorithm fuzzy c-means clustering algorithm

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Probabilistic approach for optimal placement and tuning of power system supplementary damping controllers

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IET GENERATION TRANSMISSION & DISTRIBUTION 2014年第11期8卷 1831-1842页

作者： Rueda, Jose Luis cristobal cepeda, Jaime Erlich, Istvan Delft Univ Technol Dept Elect Sustainable Energy NL-2600 GA Delft Netherlands Corp CENACE Res & Dev Dept Quito 171112 Ecuador Univ Duisburg Essen Inst Elect Power Syst D-47057 Duisburg Germany

This study presents a comprehensive approach to tackle the problem of optimal placement and coordinated tuning of power system supplementary damping controllers (OPcTSDc). The approach uses a recursive framework comprising probabilistic eigenanalysis (PE), a scenario selection technique (SST) and a new variant of mean-variance mapping optimisation algorithm (MVMO-SM). Based on probabilistic models used to sample a wide range of operating conditions, PE is applied to determine the instability risk because of poorly-damped oscillatory modes. Next, the insights gathered from PE are exploited by SST, which combines principal component analysis and fuzzy c-means clustering algorithm to extract a reduced subset of representative scenarios. The multi-scenario formulation of OPcTSDc is then solved by MVMO-SM. A case study on the New England test system, which includes performance comparisons between different modern heuristic optimisation algorithms, illustrates the feasibility and effectiveness of the proposed approach.

关键词： power system control probability power engineering computing eigenvalues and eigenfunctions optimisation power system stability fuzzy set theory pattern clustering probabilistic approach power system supplementary damping controllers coordinated tuning optimal placement problem OPcTSDc probabilistic eigenanalysis PE recursive framework scenario selection technique SST mean-variance mapping optimisation algorithm MVMO-SM instability risk poorly-damped oscillatory modes principal component analysis fuzzy c-means clustering algorithm New England test system heuristic optimisation algorithms

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Investigating the Relationship between Therapeutic Music and Emotion: A Pilot Study on Healthcare Service 21

Investigating the Relationship between Therapeutic Music and...

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21st ISPE Inc International conference on concurrent Engineering

作者： Hsu, Ya-Wen chiu, Ming-chuan Hwang, Sheue-Ling Natl Tsing Hua Univ Hsinchu 30013 Taiwan

ISBN: (纸本)9781614994404;9781614994398

Life of modern people becomes more convenient and rich in material side but worse in mental side due to life stress. This results in bloom of some diseases such as insomnia. Listening to music could be one way to make people feel smooth. Some previous literature had advocated the efficiency of music therapy, however, only a few previous studies discussed and connected personal cognition (subjective indicators) with music features (objective indicators). Therefore, the aim of the study is to investigate what kind of music characteristics can spiritually relax people and obtain the therapeutic music from above results. Firstly, this study collected 25 different styles of music as samples. These songs were classified with fuzzy c-means clustering algorithm. According to our experimental result, music with mild amplitude, slow speed, and positive feelings can enable soothing in mind. The findings would also fit in with physiological signals (Heart Rate Variability) to ensure the consistency in psychology and physiology. This finding can provide suggestions on selection of therapeutic music. In addition, musicians can compose appropriate therapeutic music for patients of different mental illness.

关键词： Music Therapy fuzzy c-means clustering algorithm Personal cognition Heart rate variability (HRV)

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