Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295339
D. Stavrakoudis, P. Mastorocostas, Ioannis B. Theocharis
This paper presents a recurrent fuzzy-neural filter that performs the task of separation of lung sounds, obtained from patients with pulmonary pathology. The filter is a pipelined Takagi-Sugeno-Kang recurrent fuzzy network, consisting of a number of modules interconnected in a cascaded form. The participating modules are implemented through recurrent fuzzy neural networks with internal dynamics. The structure of the modules is evolved sequentially from input-output data. Extensive experimental results, regarding the lung sound category of crackles, are given, and a performance comparison with a series of other fuzzy and neural filters is conducted, underlining the separation capabilities of the proposed filter.
{"title":"A Pipelined Recurrent Fuzzy Neural Filter for the Separation of Lung Sounds","authors":"D. Stavrakoudis, P. Mastorocostas, Ioannis B. Theocharis","doi":"10.1109/FUZZY.2007.4295339","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295339","url":null,"abstract":"This paper presents a recurrent fuzzy-neural filter that performs the task of separation of lung sounds, obtained from patients with pulmonary pathology. The filter is a pipelined Takagi-Sugeno-Kang recurrent fuzzy network, consisting of a number of modules interconnected in a cascaded form. The participating modules are implemented through recurrent fuzzy neural networks with internal dynamics. The structure of the modules is evolved sequentially from input-output data. Extensive experimental results, regarding the lung sound category of crackles, are given, and a performance comparison with a series of other fuzzy and neural filters is conducted, underlining the separation capabilities of the proposed filter.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114057056","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295444
J. Beringer, E. Hüllermeier
In this paper, we present a local, adaptive optimization scheme for adjusting the number of clusters in fuzzy C-means clustering. This method is especially motivated by online applications in which a potentially changing clustering structure must be maintained over time, though it turns out to be useful in the static case as well. As part of the method, we propose a new validity measure for fuzzy partitions which is a modification of the commonly used Xie-Beni index and overcomes some deficiencies thereof.
{"title":"Adaptive Optimization of the Number of Clusters in Fuzzy Clustering","authors":"J. Beringer, E. Hüllermeier","doi":"10.1109/FUZZY.2007.4295444","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295444","url":null,"abstract":"In this paper, we present a local, adaptive optimization scheme for adjusting the number of clusters in fuzzy C-means clustering. This method is especially motivated by online applications in which a potentially changing clustering structure must be maintained over time, though it turns out to be useful in the static case as well. As part of the method, we propose a new validity measure for fuzzy partitions which is a modification of the commonly used Xie-Beni index and overcomes some deficiencies thereof.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128499424","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295636
B. Bedregal, R. Santiago, R. Reiser, G. Dimuro
The aim of this work is to analyze interval fuzzy S-implications and interval automorphisms. Starting from any fuzzy S-implication, it is shown how to obtain an interval fuzzy S-implication canonically. We proved that such interval fuzzy S-implications meet the optimality property and preserve the same properties satisfied by fuzzy S-implications. In addition, commutative diagrams are used in order to relate fuzzy S-implications to interval fuzzy S-implications, and to understand how interval automorphisms act on interval S-implications, generating other interval fuzzy S-implications.
{"title":"The Best Interval Representation of Fuzzy S-Implications and Automorphisms","authors":"B. Bedregal, R. Santiago, R. Reiser, G. Dimuro","doi":"10.1109/FUZZY.2007.4295636","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295636","url":null,"abstract":"The aim of this work is to analyze interval fuzzy S-implications and interval automorphisms. Starting from any fuzzy S-implication, it is shown how to obtain an interval fuzzy S-implication canonically. We proved that such interval fuzzy S-implications meet the optimality property and preserve the same properties satisfied by fuzzy S-implications. In addition, commutative diagrams are used in order to relate fuzzy S-implications to interval fuzzy S-implications, and to understand how interval automorphisms act on interval S-implications, generating other interval fuzzy S-implications.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128581161","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295621
I. Baturone, A. Gersnoviez
This paper describes how the combination of neuro-fuzzy techniques with geometric analysis offers a good trade-off between purely heuristics and purely physical approaches when solving the problem of car-like robot navigation. The controller described, which follows a reactive technique, generates trajectories of near-minimal lengths when no obstacles are detected and, in presence of obstacles, generates minimum deviations from them. All these reference paths meet the kinematic constraints of car-like robots and take into account dynamic issues. Besides its efficiency, the proposed controller is very simple and linguistically interpretable. The whole controller has been designed and verified by using the CAD tools of the Xfuzzy environment.
{"title":"A Simple Neuro-Fuzzy Controller for Car-Like Robot Navigation Avoiding Obstacles","authors":"I. Baturone, A. Gersnoviez","doi":"10.1109/FUZZY.2007.4295621","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295621","url":null,"abstract":"This paper describes how the combination of neuro-fuzzy techniques with geometric analysis offers a good trade-off between purely heuristics and purely physical approaches when solving the problem of car-like robot navigation. The controller described, which follows a reactive technique, generates trajectories of near-minimal lengths when no obstacles are detected and, in presence of obstacles, generates minimum deviations from them. All these reference paths meet the kinematic constraints of car-like robots and take into account dynamic issues. Besides its efficiency, the proposed controller is very simple and linguistically interpretable. The whole controller has been designed and verified by using the CAD tools of the Xfuzzy environment.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128678368","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295517
G. Coghill
Fuzzy qualitative simulation combines the features of qualitative simulation and fuzzy reasoning in order to gain advantages from both. However, the output of a fuzzy qualitative simulation process is a behaviour tree which for complex systems will be large. In order to overcome this and permit focussing on preferred behaviours priortisation was developed. In this paper a new prioritisation scheme is presented that makes use of both constraint and temporal information to perform the prioritisation.
{"title":"Fuzzy Qualitative Behaviour Prioritisation","authors":"G. Coghill","doi":"10.1109/FUZZY.2007.4295517","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295517","url":null,"abstract":"Fuzzy qualitative simulation combines the features of qualitative simulation and fuzzy reasoning in order to gain advantages from both. However, the output of a fuzzy qualitative simulation process is a behaviour tree which for complex systems will be large. In order to overcome this and permit focussing on preferred behaviours priortisation was developed. In this paper a new prioritisation scheme is presented that makes use of both constraint and temporal information to perform the prioritisation.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124636440","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295388
Xuemei Zheng, J. Platts, Yong Feng
The robotic system of a two-link flexible manipulator is decomposed into an input-output subsystem and a zero dynamics subsystem using the input-output linearization technique. A novel inverse dynamics nonsingular terminal sliding mode controller is designed to make the input-output subsystem converge to its equilibrium point in finite time. The parameters of the zero dynamic subsystem are optimized by a genetic algorithm so that the zero dynamics subsystem is asymptotically stable at equilibrium point and finally the whole original flexible manipulator system is guaranteed to be asymptotically stable. Additionally, in order to overcome the chattering, this paper adapts a fuzzy logic controller to realize the nonlinear switching function. Simulation results are presented to validate the design.
{"title":"Based on Parameter Optimization and FLC Nonsingular Terminal Sliding Mode Controller of a Two-Link Flexible Manipulator","authors":"Xuemei Zheng, J. Platts, Yong Feng","doi":"10.1109/FUZZY.2007.4295388","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295388","url":null,"abstract":"The robotic system of a two-link flexible manipulator is decomposed into an input-output subsystem and a zero dynamics subsystem using the input-output linearization technique. A novel inverse dynamics nonsingular terminal sliding mode controller is designed to make the input-output subsystem converge to its equilibrium point in finite time. The parameters of the zero dynamic subsystem are optimized by a genetic algorithm so that the zero dynamics subsystem is asymptotically stable at equilibrium point and finally the whole original flexible manipulator system is guaranteed to be asymptotically stable. Additionally, in order to overcome the chattering, this paper adapts a fuzzy logic controller to realize the nonlinear switching function. Simulation results are presented to validate the design.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130678287","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295465
M. Cococcioni, P. Ducange, B. Lazzerini, F. Marcelloni
An approach to select the most suitable fuzzy rule-based binary classifier to a specific application is proposed. First, an evolutionary three-objective optimization algorithm is applied to generate an approximation of a Pareto front composed of fuzzy rule-based binary classifiers with different trade-offs between accuracy and complexity. Accuracy is measured in terms of sensitivity and specificity, whereas complexity is computed as sum of the conditions which compose the antecedents of the rules included in the classifiers. Thus, low values of complexity correspond to fuzzy systems characterized by a low number of rules and a low number of input variables actually used in each rule. This ensures a high comprehensibility of the classifiers. Then, the most suitable classifier is selected by using the ROC convex hull method. We discuss the application of the proposed approach to generate a classifier for discriminating lung nodules from non-nodules in a computer aided diagnosis (CAD) system. Results obtained on a real data set extracted from lung CT images are also discussed
{"title":"Evolutionary Multi-Objective Optimization of Fuzzy Rule-Based Classifiers in the ROC Space","authors":"M. Cococcioni, P. Ducange, B. Lazzerini, F. Marcelloni","doi":"10.1109/FUZZY.2007.4295465","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295465","url":null,"abstract":"An approach to select the most suitable fuzzy rule-based binary classifier to a specific application is proposed. First, an evolutionary three-objective optimization algorithm is applied to generate an approximation of a Pareto front composed of fuzzy rule-based binary classifiers with different trade-offs between accuracy and complexity. Accuracy is measured in terms of sensitivity and specificity, whereas complexity is computed as sum of the conditions which compose the antecedents of the rules included in the classifiers. Thus, low values of complexity correspond to fuzzy systems characterized by a low number of rules and a low number of input variables actually used in each rule. This ensures a high comprehensibility of the classifiers. Then, the most suitable classifier is selected by using the ROC convex hull method. We discuss the application of the proposed approach to generate a classifier for discriminating lung nodules from non-nodules in a computer aided diagnosis (CAD) system. Results obtained on a real data set extracted from lung CT images are also discussed","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123881515","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295473
M. E. Valle, P. Sussner
Mathematical morphology (MM) is a theory concerned with the processing and analysis of objects using operators based on topological and geometrical concepts. We speak of a fuzzy morphological associative memory (FMAM) when a fuzzy associative memory (FAM) model is equipped with neurons that correspond to an operator of mathematical morphology. This paper shows that several FAM models, including the FAMs of Kosko, most generalized FAMs of Chung and Lee, the FAM of Junbo et al., the max-min FAM with threshold, the fuzzy logic bidirectional associative memories, and the implicative fuzzy associative memories, belong to the FMAM class. Moreover, we present two strategies for deriving a new FMAM model from a given FMAM. These strategies are based on two duality relationship of mathematical morphology: duality with respect to negation and duality with respect to adjunction.
{"title":"Fuzzy Associative Memories from the Perspective of Mathematical Morphology","authors":"M. E. Valle, P. Sussner","doi":"10.1109/FUZZY.2007.4295473","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295473","url":null,"abstract":"Mathematical morphology (MM) is a theory concerned with the processing and analysis of objects using operators based on topological and geometrical concepts. We speak of a fuzzy morphological associative memory (FMAM) when a fuzzy associative memory (FAM) model is equipped with neurons that correspond to an operator of mathematical morphology. This paper shows that several FAM models, including the FAMs of Kosko, most generalized FAMs of Chung and Lee, the FAM of Junbo et al., the max-min FAM with threshold, the fuzzy logic bidirectional associative memories, and the implicative fuzzy associative memories, belong to the FMAM class. Moreover, we present two strategies for deriving a new FMAM model from a given FMAM. These strategies are based on two duality relationship of mathematical morphology: duality with respect to negation and duality with respect to adjunction.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123727538","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295429
S. Lei, A. Palazzolo, A. Kascak
This paper presents a fuzzy logic based intelligent control system applied to magnetic bearings. The core in the expert system is fuzzy logic controllers with Mamdani architecture. The fuzzy logic controllers for rub detection and automatic gain scheduling were implemented. The expert system not only provides a means to capture the run time data of the magnetic bearings, to process and monitor the parameters, and to diagnose malfunctions, but also protects the magnetic bearings from rub anomaly and implements the control on a real time basis.
{"title":"Fuzzy Logic Intelligent Control System of Magnetic Bearings","authors":"S. Lei, A. Palazzolo, A. Kascak","doi":"10.1109/FUZZY.2007.4295429","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295429","url":null,"abstract":"This paper presents a fuzzy logic based intelligent control system applied to magnetic bearings. The core in the expert system is fuzzy logic controllers with Mamdani architecture. The fuzzy logic controllers for rub detection and automatic gain scheduling were implemented. The expert system not only provides a means to capture the run time data of the magnetic bearings, to process and monitor the parameters, and to diagnose malfunctions, but also protects the magnetic bearings from rub anomaly and implements the control on a real time basis.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121507370","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295587
J. Yoneyama
This paper is concerned with robust H∞ filtering for uncertain fuzzy time-delay systems. Robust filtering conditions and a design method of robust H∞ filter are given. A system that is considered in this paper is an uncertain fuzzy system with time-delays in state and output. The time-delay is assumed to be either known constant or unknown time varying. The parameter uncertainties that come into the system are time varying and describe identification error between a real system and its mathematical model. Our approach employs a Lyapunov functional combined with the parameterized model transformation method and the generalized free weighting matrix method. This generalization leads to a generalized robust H∞ filtering condition that is given in terms of linear matrix inequalities. Moreover, based on such a condition, robust H∞ filtering methods for uncertain fuzzy systems with time-delays are given. A numerical example is given to illustrate our robust H∞ filtering methods.
{"title":"Robust H∞ Filtering for Fuzzy Time-Delay Systems","authors":"J. Yoneyama","doi":"10.1109/FUZZY.2007.4295587","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295587","url":null,"abstract":"This paper is concerned with robust H∞ filtering for uncertain fuzzy time-delay systems. Robust filtering conditions and a design method of robust H∞ filter are given. A system that is considered in this paper is an uncertain fuzzy system with time-delays in state and output. The time-delay is assumed to be either known constant or unknown time varying. The parameter uncertainties that come into the system are time varying and describe identification error between a real system and its mathematical model. Our approach employs a Lyapunov functional combined with the parameterized model transformation method and the generalized free weighting matrix method. This generalization leads to a generalized robust H∞ filtering condition that is given in terms of linear matrix inequalities. Moreover, based on such a condition, robust H∞ filtering methods for uncertain fuzzy systems with time-delays are given. A numerical example is given to illustrate our robust H∞ filtering methods.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"40 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113999943","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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