Pub Date : 2015-01-06DOI: 10.1109/FUZZY.2009.5277404
S. Abuelenin
In this paper we present a Fuzzy Logic control approach designed to stabilize a multi-input multi-output magnetic suspension system. The system has four cubic floaters and four actuators that apply magnetic forces on the floaters, the suspension is performed by changing the voltages applied on the actuators, hence changing their currents, producing vertical magnetic forces that balance with the gravitational force. A fuzzy logic controller is used to control each actuator; the system is nonlinear and sensitive to initial conditions. Another fuzzy logic controller is used as a supervisory controller in order to increase the dynamic range of the system, enabling it to stabilize the floaters when the initial displacements are relatively big. Another design consideration was to keep the four floaters in the same plane as much as possible, to perform that task, a PD controller was set to modulate the currents of the four actuators in order to minimize an error signal measuring the relative vertical displacement of all the four floaters. Simulation results show that the designed control scheme stabilized the system for the design constrains.
{"title":"Design and simulation of a hybrid controller for a multi-input multi-output magnetic suspension system","authors":"S. Abuelenin","doi":"10.1109/FUZZY.2009.5277404","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277404","url":null,"abstract":"In this paper we present a Fuzzy Logic control approach designed to stabilize a multi-input multi-output magnetic suspension system. The system has four cubic floaters and four actuators that apply magnetic forces on the floaters, the suspension is performed by changing the voltages applied on the actuators, hence changing their currents, producing vertical magnetic forces that balance with the gravitational force. A fuzzy logic controller is used to control each actuator; the system is nonlinear and sensitive to initial conditions. Another fuzzy logic controller is used as a supervisory controller in order to increase the dynamic range of the system, enabling it to stabilize the floaters when the initial displacements are relatively big. Another design consideration was to keep the four floaters in the same plane as much as possible, to perform that task, a PD controller was set to modulate the currents of the four actuators in order to minimize an error signal measuring the relative vertical displacement of all the four floaters. Simulation results show that the designed control scheme stabilized the system for the design constrains.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130874482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277306
Hwachang Song, Ryan B. Diolata, Y. Joo
This paper presents a novel multi-level quantization scheme which best approximates the sigmoid function for multi-value discrete variable transformation in PSO. We define the set of multi-level quantization as integer multiples of powers-of-two terms to efficiently approximate the sigmoid function in transforming particle's position into multilevel discrete values. In this paper, the feasibility of the proposed technique was tested in photovoltaic (PV) system allocation problem, and a comparison study with genetic algorithm (GA) is performed to show the quality of the solutions obtained.
{"title":"A novel multi-level quantization scheme for discrete particle swarm optimization","authors":"Hwachang Song, Ryan B. Diolata, Y. Joo","doi":"10.1109/FUZZY.2009.5277306","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277306","url":null,"abstract":"This paper presents a novel multi-level quantization scheme which best approximates the sigmoid function for multi-value discrete variable transformation in PSO. We define the set of multi-level quantization as integer multiples of powers-of-two terms to efficiently approximate the sigmoid function in transforming particle's position into multilevel discrete values. In this paper, the feasibility of the proposed technique was tested in photovoltaic (PV) system allocation problem, and a comparison study with genetic algorithm (GA) is performed to show the quality of the solutions obtained.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127168374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277386
C. Kung, J. Su, Yi-Fen Nieh
This paper proposed a novel cluster validity criterion for fuzzy c-regression models (FCRM) clustering algorithm with hyper-plane-shaped clusters. We combined the concept of fuzzy hypervolume with the compactness validity function in the cluster validity criterion. The proposed cluster validity criterion determined the appropriate number of clusters by calculating the overall compactness and separateness of the FCRM partition. The simulation results demonstrated the validness and effectiveness of the proposed method.
{"title":"A novel cluster validity criterion for fuzzy C-regression models","authors":"C. Kung, J. Su, Yi-Fen Nieh","doi":"10.1109/FUZZY.2009.5277386","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277386","url":null,"abstract":"This paper proposed a novel cluster validity criterion for fuzzy c-regression models (FCRM) clustering algorithm with hyper-plane-shaped clusters. We combined the concept of fuzzy hypervolume with the compactness validity function in the cluster validity criterion. The proposed cluster validity criterion determined the appropriate number of clusters by calculating the overall compactness and separateness of the FCRM partition. The simulation results demonstrated the validness and effectiveness of the proposed method.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123315565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277081
K. Rasmani, J. Garibaldi, Qiang Shen, Ian O. Ellis
The use of linguistic rulesets is considered one of the greatest advantages that fuzzy classification systems can offer compared to non-fuzzy classification systems. This paper proposes the use of fuzzy thresholds and fuzzy quantifiers for generating linguistic rulesets from a data-driven fuzzy subsethood-based classification system. The proposed technique offers not only simplicity in the design and comprehensibility of the generated rulesets but also practicality in the implementation. Additionally, the use of fuzzy quantifiers makes it easier for the user to understand the classification process and how such classifications were reached. The effectiveness of the proposed method is demonstrated using a medical dataset which provides evidence that rules generated by the proposed system are consistent with the expert-rules created by clinicians.
{"title":"Linguistic rulesets extracted from a quantifier-based fuzzy classification system","authors":"K. Rasmani, J. Garibaldi, Qiang Shen, Ian O. Ellis","doi":"10.1109/FUZZY.2009.5277081","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277081","url":null,"abstract":"The use of linguistic rulesets is considered one of the greatest advantages that fuzzy classification systems can offer compared to non-fuzzy classification systems. This paper proposes the use of fuzzy thresholds and fuzzy quantifiers for generating linguistic rulesets from a data-driven fuzzy subsethood-based classification system. The proposed technique offers not only simplicity in the design and comprehensibility of the generated rulesets but also practicality in the implementation. Additionally, the use of fuzzy quantifiers makes it easier for the user to understand the classification process and how such classifications were reached. The effectiveness of the proposed method is demonstrated using a medical dataset which provides evidence that rules generated by the proposed system are consistent with the expert-rules created by clinicians.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123324720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277428
Tsung-Ying Sun, Ming-Chin Yang, Shang-Jeng Tsai, Jyun-Sian He
The Flush Material Belt Weigh Feeder (FMBWF) has used in many material handling plants. The stability and the performance of the layer control system will affect the quality of the production. In general, the behavior of the flush material on the BWF is non-linear, time-lag, and disturbance character. The layer of the flush material on the belt is hard to be stably controlled especially the occurrence of unstable situation in flush material or the prefeeder feeding rate and the variation of the set point value of the FMBWF. This paper focuses on the performance improvement of the FMBWF via adopted a fuzzy controller under the situation of the Flow Rate Set Value (FRSV) variation. The proposed fuzzy controller is utilized instead of the original feed forward compensation function to deal with the problems of the temporary unstable situation. This paper offers the simulation result comparison of the set point value variations between the BWF for the original and the proposed control system. The simulation results are distinct better than the original control system. The influence of the variations of the set point value can be successfully evaluated by the Fuzzy controller and adjusted the Proportional Actuator immediately. Finally the proposed control system remains convergence with smooth and stable. Therefore, the accuracy, stability and the performance of the control system are improved.
{"title":"An improved flush material belt weigh feeder system via fuzzy logic controller and adaptive neural networks","authors":"Tsung-Ying Sun, Ming-Chin Yang, Shang-Jeng Tsai, Jyun-Sian He","doi":"10.1109/FUZZY.2009.5277428","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277428","url":null,"abstract":"The Flush Material Belt Weigh Feeder (FMBWF) has used in many material handling plants. The stability and the performance of the layer control system will affect the quality of the production. In general, the behavior of the flush material on the BWF is non-linear, time-lag, and disturbance character. The layer of the flush material on the belt is hard to be stably controlled especially the occurrence of unstable situation in flush material or the prefeeder feeding rate and the variation of the set point value of the FMBWF. This paper focuses on the performance improvement of the FMBWF via adopted a fuzzy controller under the situation of the Flow Rate Set Value (FRSV) variation. The proposed fuzzy controller is utilized instead of the original feed forward compensation function to deal with the problems of the temporary unstable situation. This paper offers the simulation result comparison of the set point value variations between the BWF for the original and the proposed control system. The simulation results are distinct better than the original control system. The influence of the variations of the set point value can be successfully evaluated by the Fuzzy controller and adjusted the Proportional Actuator immediately. Finally the proposed control system remains convergence with smooth and stable. Therefore, the accuracy, stability and the performance of the control system are improved.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115027501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277426
Jin-Tsong Jeng, Chen-Chia Chuang, Sheng-Lun Jheng
In this paper, the hybrid support vector machines (SVM) and Gaussian process (GPs) are proposed to deal with the molecular autoregulatory feedback loop systems with outliers. In the proposed approach, there are two-stage strategies. In the stage 1, the support vector machine regression (SVMR) approach is used to filter out the outliers in the training data set. Because of the large outliers in the training data set are almost removed, the large outlier's effects are reduce, so the concepts of robust statistic theory are not used to reduce the outlier's effects. The rest of the training data set after the stage 1 is directly used to training the Gaussian process for regression (GPR) in the stage 2. According to the simulation results, the performance of the proposed approach is superior to the least squares support vector machines for regression, and GPR when the outliers are existed in the molecular autoregulatory feedback loop systems.
{"title":"Hybrid SVM-GPs learning for modeling of molecular autoregulatory feedback loop systems with outliers","authors":"Jin-Tsong Jeng, Chen-Chia Chuang, Sheng-Lun Jheng","doi":"10.1109/FUZZY.2009.5277426","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277426","url":null,"abstract":"In this paper, the hybrid support vector machines (SVM) and Gaussian process (GPs) are proposed to deal with the molecular autoregulatory feedback loop systems with outliers. In the proposed approach, there are two-stage strategies. In the stage 1, the support vector machine regression (SVMR) approach is used to filter out the outliers in the training data set. Because of the large outliers in the training data set are almost removed, the large outlier's effects are reduce, so the concepts of robust statistic theory are not used to reduce the outlier's effects. The rest of the training data set after the stage 1 is directly used to training the Gaussian process for regression (GPR) in the stage 2. According to the simulation results, the performance of the proposed approach is superior to the least squares support vector machines for regression, and GPR when the outliers are existed in the molecular autoregulatory feedback loop systems.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114125990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277097
Ying-Jen Chen, H. Ohtake, Kazuo Tanaka, Wen-June Wang, Hua O. Wang
Based on the piecewise Lyapunov function, this study proposes a switching fuzzy controller, which switches depending on the Lyapunov function, to get relaxed stabilization conditions for the continuous T-S fuzzy system. The relaxed conditions are bilinear with the s-procedure parameters, therefore the particle swarm optimization (PSO) algorithm is utilized with the LMI tool to solve the relaxed conditions. Two simulation examples are given to show the relaxation and effectiveness of the proposed method.
{"title":"Relaxed stabilization conditions of T-S fuzzy systems using piecewise lyapunov function based switching fuzzy controller","authors":"Ying-Jen Chen, H. Ohtake, Kazuo Tanaka, Wen-June Wang, Hua O. Wang","doi":"10.1109/FUZZY.2009.5277097","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277097","url":null,"abstract":"Based on the piecewise Lyapunov function, this study proposes a switching fuzzy controller, which switches depending on the Lyapunov function, to get relaxed stabilization conditions for the continuous T-S fuzzy system. The relaxed conditions are bilinear with the s-procedure parameters, therefore the particle swarm optimization (PSO) algorithm is utilized with the LMI tool to solve the relaxed conditions. Two simulation examples are given to show the relaxation and effectiveness of the proposed method.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116741706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277307
N. Baklouti, A. Alimi
Recently type-2 Fuzzy logic systems (FLSs) have demonstrated their competence in treating vagueness in real world dynamic systems. But, in the last few years, new trends and theory in Fuzzy Logic have been appeared, proposing the geometric type-2 Fuzzy logic approach. The main idea of this approach was to model fuzzy logic sets using computational geometry providing by this more accurate results and better performance in treating vagueness. Throughout this paper, we study the effect of the geometric approach in robotic mobile issue. We propose two controllers: a geometric interval type-2 fuzzy logic local avoiding obstacles controller and a geometric interval type-2 fuzzy logic wall following controller. The obtained results are presented and are discussed. The geometric type-2 FLSs provide good results…
{"title":"The geometric interval type-2 fuzzy logic approach in robotic mobile issue","authors":"N. Baklouti, A. Alimi","doi":"10.1109/FUZZY.2009.5277307","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277307","url":null,"abstract":"Recently type-2 Fuzzy logic systems (FLSs) have demonstrated their competence in treating vagueness in real world dynamic systems. But, in the last few years, new trends and theory in Fuzzy Logic have been appeared, proposing the geometric type-2 Fuzzy logic approach. The main idea of this approach was to model fuzzy logic sets using computational geometry providing by this more accurate results and better performance in treating vagueness. Throughout this paper, we study the effect of the geometric approach in robotic mobile issue. We propose two controllers: a geometric interval type-2 fuzzy logic local avoiding obstacles controller and a geometric interval type-2 fuzzy logic wall following controller. The obtained results are presented and are discussed. The geometric type-2 FLSs provide good results…","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130610883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277101
Song-Shyong Chen
This paper addresses the problem of designing robust static output-feedback controllers for nonlinear network based controller design for the given T-S fuzzy model. The effects of both network-induced delays and data packet dropout will be investigated. Based on an integral inequality and a matrix inequality, a delay-dependent sufficient condition for the existence of a network-based controller is formulated in terms of a linear matrix inequality by adjusting effective parameter matrices. In the approach, we do not directly employ the Lyapunov approach, as do in most of traditional fuzzy control design approaches. Instead, sufficient conditions for guaranteeing the robust stability for the considered networked systems are derived in terms of the matrix spectral norm of the closed-loop fuzzy system. The sufficient conditions are further formulated into linear matrix inequalities so that the desired controller can be easily obtained by using the Matlab LMI toolbox. An illustrative numerical example is also given to show the effectiveness of the proposed design method.
{"title":"Networked control systems design via fuzzy logic method","authors":"Song-Shyong Chen","doi":"10.1109/FUZZY.2009.5277101","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277101","url":null,"abstract":"This paper addresses the problem of designing robust static output-feedback controllers for nonlinear network based controller design for the given T-S fuzzy model. The effects of both network-induced delays and data packet dropout will be investigated. Based on an integral inequality and a matrix inequality, a delay-dependent sufficient condition for the existence of a network-based controller is formulated in terms of a linear matrix inequality by adjusting effective parameter matrices. In the approach, we do not directly employ the Lyapunov approach, as do in most of traditional fuzzy control design approaches. Instead, sufficient conditions for guaranteeing the robust stability for the considered networked systems are derived in terms of the matrix spectral norm of the closed-loop fuzzy system. The sufficient conditions are further formulated into linear matrix inequalities so that the desired controller can be easily obtained by using the Matlab LMI toolbox. An illustrative numerical example is also given to show the effectiveness of the proposed design method.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116349764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277336
H. Ichihashi, A. Notsu, Katsuhiro Honda
This paper proposes an additional version of the fuzzy c-means based classifier (FCMC). The classifier FCMC-R treats relational data instead of object data. FCMCs use covariance structures to represent flexible shapes of clusters. Despite its effectiveness, the intense computation of covariance matrices is an impediment for classifying a set of high-dimensional feature data. In order to tackle with this problem, we proposed a way of directly handling high-dimensional data, i.e., FCMC-H. The third type of the FCM classifier is the relational classifier FCMC-R, which is derived from FCMC-H. The relational data represented by a relational matrix are based on dissimilarities or distances between object data. The triplets, i.e., FCMC, FCMC-H, and FCMC-R are equivalent when the dimensionality of feature vectors is not very high and the dissimilarity is represented by Euclidean distances. The randomized test set performance of FCMC on the sets of object data from UCI repository is comparable to that of the support vector machine (SVM) classifier. The performances of the triplet in terms of 100 times three way data splits (3-WDS) procedure are compared. The triplet surpasses the k-nearest neighbor (k-NN) classifier, which is a well established and very popular relational classifier.
{"title":"Triplet of FCM classifiers","authors":"H. Ichihashi, A. Notsu, Katsuhiro Honda","doi":"10.1109/FUZZY.2009.5277336","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277336","url":null,"abstract":"This paper proposes an additional version of the fuzzy c-means based classifier (FCMC). The classifier FCMC-R treats relational data instead of object data. FCMCs use covariance structures to represent flexible shapes of clusters. Despite its effectiveness, the intense computation of covariance matrices is an impediment for classifying a set of high-dimensional feature data. In order to tackle with this problem, we proposed a way of directly handling high-dimensional data, i.e., FCMC-H. The third type of the FCM classifier is the relational classifier FCMC-R, which is derived from FCMC-H. The relational data represented by a relational matrix are based on dissimilarities or distances between object data. The triplets, i.e., FCMC, FCMC-H, and FCMC-R are equivalent when the dimensionality of feature vectors is not very high and the dissimilarity is represented by Euclidean distances. The randomized test set performance of FCMC on the sets of object data from UCI repository is comparable to that of the support vector machine (SVM) classifier. The performances of the triplet in terms of 100 times three way data splits (3-WDS) procedure are compared. The triplet surpasses the k-nearest neighbor (k-NN) classifier, which is a well established and very popular relational classifier.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114828305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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