Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277106
C. Marsala
In this paper, two medical experiments are presented where the use of a fuzzy machine learning tool brought out a better understanding of the patients involved in the study. The use of fuzzy set theory to provide fuzzy labels and the construction of fuzzy decision trees to generate fuzzy rule bases enhance greatly the understandability and enable the Medical scientists to have a better understanding of the correlations between the description of the patients and their medical class. The results obtained in these two experiments highlight the usefulness of fuzzy data mining approach to handle real world data and to benefit Society.
{"title":"A fuzzy decision tree based approach to characterize medical data","authors":"C. Marsala","doi":"10.1109/FUZZY.2009.5277106","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277106","url":null,"abstract":"In this paper, two medical experiments are presented where the use of a fuzzy machine learning tool brought out a better understanding of the patients involved in the study. The use of fuzzy set theory to provide fuzzy labels and the construction of fuzzy decision trees to generate fuzzy rule bases enhance greatly the understandability and enable the Medical scientists to have a better understanding of the correlations between the description of the patients and their medical class. The results obtained in these two experiments highlight the usefulness of fuzzy data mining approach to handle real world data and to benefit Society.","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":"115067340","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.2009.5277393
Reyhaneh Madadian, M. Moghaddam
The quality of services is one of the most important issues of today's internet. The programs and the different internet users' quality of services are different from one another. Varieties of methods have been presented in providing the internet's quality of services. The Proportional Differentiated services method is one of the newest ways of providing quality of services. In this essay, a proper fuzzy model for providing quality of services in the relative differentiated services is presented. The presented method is based on the JOBS method which is one of the newest methods of services differentiation. The suggested model is implemented and analyzed in ns2 simulation environment. The final results of this simulation reveal the superiority of the suggested fuzzy method compared with the non-fuzzy one.
{"title":"Fuzzy controller design for proportional loss differentiation services","authors":"Reyhaneh Madadian, M. Moghaddam","doi":"10.1109/FUZZY.2009.5277393","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277393","url":null,"abstract":"The quality of services is one of the most important issues of today's internet. The programs and the different internet users' quality of services are different from one another. Varieties of methods have been presented in providing the internet's quality of services. The Proportional Differentiated services method is one of the newest ways of providing quality of services. In this essay, a proper fuzzy model for providing quality of services in the relative differentiated services is presented. The presented method is based on the JOBS method which is one of the newest methods of services differentiation. The suggested model is implemented and analyzed in ns2 simulation environment. The final results of this simulation reveal the superiority of the suggested fuzzy method compared with the non-fuzzy one.","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":"116286330","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.2009.5276887
H. Tizhoosh, Farhang Sahba
Opposition-based computing is the paradigm for incorporating entities along with their opposites within the search, optimization and learning mechanisms. In this work, we introduce the notion of “opposite fuzzy sets” in order to use the entropy difference between a fuzzy set and its opposite to carry out object discrimination in digital images. A quasi-global scheme is used to execute the calculations, which can be employed by any other existing thresholding technique. Results for prostate ultrasound images have been provided to verify the performance whereas expert's markings have been used as gold standard.
{"title":"Quasi-global oppositional fuzzy thresholding","authors":"H. Tizhoosh, Farhang Sahba","doi":"10.1109/FUZZY.2009.5276887","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5276887","url":null,"abstract":"Opposition-based computing is the paradigm for incorporating entities along with their opposites within the search, optimization and learning mechanisms. In this work, we introduce the notion of “opposite fuzzy sets” in order to use the entropy difference between a fuzzy set and its opposite to carry out object discrimination in digital images. A quasi-global scheme is used to execute the calculations, which can be employed by any other existing thresholding technique. Results for prostate ultrasound images have been provided to verify the performance whereas expert's markings have been used as gold standard.","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":"116441389","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.2009.5276886
G. Schaefer, T. Nakashima
Image understanding applications often involve a pattern classification stage. In this paper we show how a fuzzy rule-based classifier, extended to incorporate a cost function, can be successfully used in various imaging applications. The antecedent part of fuzzy if-then rules are specified by partitioning each attributes into fuzzy sets while the consequent class and the degree of certainty are determined from compatibility training patterns. Extension to include a cost term is shown to be straightforward and experimental results on several image processing tasks demonstrate the efficacy of our method.
{"title":"Application of cost-sensitive fuzzy classifiers to image understanding problems","authors":"G. Schaefer, T. Nakashima","doi":"10.1109/FUZZY.2009.5276886","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5276886","url":null,"abstract":"Image understanding applications often involve a pattern classification stage. In this paper we show how a fuzzy rule-based classifier, extended to incorporate a cost function, can be successfully used in various imaging applications. The antecedent part of fuzzy if-then rules are specified by partitioning each attributes into fuzzy sets while the consequent class and the degree of certainty are determined from compatibility training patterns. Extension to include a cost term is shown to be straightforward and experimental results on several image processing tasks demonstrate the efficacy of our 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":"122007256","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.2009.5277325
Pan Hongxia, Hu Jinying, Mao Hongwei
In the work process of gearbox, because the responding signal is very complex, it is difficult to extract its sensitive fault attributive information. The sensitivity of the fault degree, fault position and fault type is very different, so the characteristic parameter set constructed by the traditional characteristic extraction and analysis method is voluminous. Therefore, how to define the reliable and effective fault characteristic parameter set and how to optimize the parameter set by the sensitive degree are the await solved problems to realize real time and online fault diagnosis. In this paper, the characteristic extractive method base on particle swarm optimization (PSO) is presented for the problem of gearbox failure characteristic selection. Then the technology is applied to analyze and process the vibration responding signal of gearbox, extract and optimize the fault characteristic parameter set. Finally the parameter set nearly related to the gearbox's fault is constructed and it is used to the fault diagnosis. It proves validity of the diagnosis result that PSO algorithm has good effectiveness, higher diagnosis precision and fast optimal speed than the traditional genetic algorithm, The experimental result indicates that the wavelet neural network training method based on the PSO algorithm is an effective training algorithm, and meanwhile it is also an available approach to solve fault diagnosis problems.
{"title":"Research of fault-characteristic extractive technology based on particle swarm optimization","authors":"Pan Hongxia, Hu Jinying, Mao Hongwei","doi":"10.1109/FUZZY.2009.5277325","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277325","url":null,"abstract":"In the work process of gearbox, because the responding signal is very complex, it is difficult to extract its sensitive fault attributive information. The sensitivity of the fault degree, fault position and fault type is very different, so the characteristic parameter set constructed by the traditional characteristic extraction and analysis method is voluminous. Therefore, how to define the reliable and effective fault characteristic parameter set and how to optimize the parameter set by the sensitive degree are the await solved problems to realize real time and online fault diagnosis. In this paper, the characteristic extractive method base on particle swarm optimization (PSO) is presented for the problem of gearbox failure characteristic selection. Then the technology is applied to analyze and process the vibration responding signal of gearbox, extract and optimize the fault characteristic parameter set. Finally the parameter set nearly related to the gearbox's fault is constructed and it is used to the fault diagnosis. It proves validity of the diagnosis result that PSO algorithm has good effectiveness, higher diagnosis precision and fast optimal speed than the traditional genetic algorithm, The experimental result indicates that the wavelet neural network training method based on the PSO algorithm is an effective training algorithm, and meanwhile it is also an available approach to solve fault diagnosis problems.","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":"124764144","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.2009.5277422
Shuming Wang, J. Watada
A new class of fuzzy stochastic optimization models — two-stage fuzzy stochastic programming with Value-at-Risk (VaR) criteria is established in this paper. An approximation algorithm is proposed to compute the VaR by combining discretization method of fuzzy variable, random simulation technique and bisection method. The convergence theorem of the approximation algorithm is also proved. To solve the two-stage fuzzy stochastic programming problems with VaR criteria, we integrate the approximation algorithm, neural network (NN) and particle swarm optimization (PSO) algorithm, and hence produce a hybrid PSO algorithm to search for the optimal solution. A numerical example is provided to illustrate the designed hybrid PSO algorithm.
{"title":"Value-at-risk-based fuzzy stochastic optimization problems","authors":"Shuming Wang, J. Watada","doi":"10.1109/FUZZY.2009.5277422","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277422","url":null,"abstract":"A new class of fuzzy stochastic optimization models — two-stage fuzzy stochastic programming with Value-at-Risk (VaR) criteria is established in this paper. An approximation algorithm is proposed to compute the VaR by combining discretization method of fuzzy variable, random simulation technique and bisection method. The convergence theorem of the approximation algorithm is also proved. To solve the two-stage fuzzy stochastic programming problems with VaR criteria, we integrate the approximation algorithm, neural network (NN) and particle swarm optimization (PSO) algorithm, and hence produce a hybrid PSO algorithm to search for the optimal solution. A numerical example is provided to illustrate the designed hybrid PSO algorithm.","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":"127370573","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.2009.5277343
Juan M. Bardallo, Miguel A. De Vega, F. A. Márquez, A. Peregrín
In this paper we present a parallel evolutionary multi-objective methodology for granularity and rule-based learning for Mamdani Fuzzy Systems. The proposed methodology produces a set of solutions with different trade-off between accuracy and interpretability, based on searching the number of labels and the fuzzy rules, and also makes a variable selection. This process is achieved by exploiting present parallel computer systems allowing it to deal with more complex models.
{"title":"Parallel evolutionary multiobjective methodology for granularity and rule base learning in linguistic fuzzy systems","authors":"Juan M. Bardallo, Miguel A. De Vega, F. A. Márquez, A. Peregrín","doi":"10.1109/FUZZY.2009.5277343","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277343","url":null,"abstract":"In this paper we present a parallel evolutionary multi-objective methodology for granularity and rule-based learning for Mamdani Fuzzy Systems. The proposed methodology produces a set of solutions with different trade-off between accuracy and interpretability, based on searching the number of labels and the fuzzy rules, and also makes a variable selection. This process is achieved by exploiting present parallel computer systems allowing it to deal with more complex models.","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":"128989733","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.2009.5277162
M. Song, Jin Bae Park, Y. Joo, Jin-Kyu Kim
In this paper, the stability analysis and stabilization problem for a discrete-time Markovian jump nonlinear systems (MJLNS) with time-varying delays are investigated. The time-delay is considered to be time-varying and has a upper bound. The transition probabilities of the mode jumps are considered to be completely known. Sufficient conditions for stochastic stability of the markovian jump fuzzy systems (MJFS) are derived via the linear matrix inequality (LMI) formulation, and the design of the stabilizing controller is further given. A numerical example is used to illustrate the developed theory.
{"title":"State feedback fuzzy-model-based control for discrete-time markovian jump nonlinear systems with time-varying delays","authors":"M. Song, Jin Bae Park, Y. Joo, Jin-Kyu Kim","doi":"10.1109/FUZZY.2009.5277162","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277162","url":null,"abstract":"In this paper, the stability analysis and stabilization problem for a discrete-time Markovian jump nonlinear systems (MJLNS) with time-varying delays are investigated. The time-delay is considered to be time-varying and has a upper bound. The transition probabilities of the mode jumps are considered to be completely known. Sufficient conditions for stochastic stability of the markovian jump fuzzy systems (MJFS) are derived via the linear matrix inequality (LMI) formulation, and the design of the stabilizing controller is further given. A numerical example is used to illustrate the developed theory.","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":"128886468","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.2009.5277367
C. Kung, Ti-Hung Chen, Liang-Chih Huang
This paper proposes an adaptive fuzzy sliding-mode controller for a class of underactuated systems. Here, the underactuated system is decoupled into two subsystems, and respectively define a sliding surface for each subsystem. The fuzzy models are applied to estimate the unknown functions of the controlled underactuated system. Then, we will propose the adaptive fuzzy sliding model controller to guarantee the tracking performance. Finally, computer simulations are given to demonstrate the tracking performance of the proposed control strategy.
{"title":"Adaptive fuzzy sliding mode control for a class of underactuated systems","authors":"C. Kung, Ti-Hung Chen, Liang-Chih Huang","doi":"10.1109/FUZZY.2009.5277367","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277367","url":null,"abstract":"This paper proposes an adaptive fuzzy sliding-mode controller for a class of underactuated systems. Here, the underactuated system is decoupled into two subsystems, and respectively define a sliding surface for each subsystem. The fuzzy models are applied to estimate the unknown functions of the controlled underactuated system. Then, we will propose the adaptive fuzzy sliding model controller to guarantee the tracking performance. Finally, computer simulations are given to demonstrate the tracking performance of the proposed control strategy.","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":"130703482","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.2009.5277353
Kuo-Lung Wu
In the traditional fuzzy c-means clustering algorithm, nearly no data points have a membership value one. Özdemir and Akarum proposed a partition index maximization (PIM) algorithm which allows the data points can whole belonging to one cluster. This modification can form a core for each cluster and data points inside the core will have membership value {0,1}. In this paper, we will discuss the parameter selection problems and robust properties of the PIM algorithm.
{"title":"An analysis of partition index maximization algorithm","authors":"Kuo-Lung Wu","doi":"10.1109/FUZZY.2009.5277353","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277353","url":null,"abstract":"In the traditional fuzzy c-means clustering algorithm, nearly no data points have a membership value one. Özdemir and Akarum proposed a partition index maximization (PIM) algorithm which allows the data points can whole belonging to one cluster. This modification can form a core for each cluster and data points inside the core will have membership value {0,1}. In this paper, we will discuss the parameter selection problems and robust properties of the PIM algorithm.","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":"132032289","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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