Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1209366
F. J. Moreno-Velo, I. Baturone, R. Senhadji, S. Sánchez-Solano
Tuning a fuzzy system to meet a given set of input/output patterns is usually a difficult task that involves many parameters. This paper presents an study of different approaches that can be applied to perform this tuning process automatically, and describes a CAD tool, named xfsl, which allows applying a wide set of these approaches: (a) a large number of supervised learning algorithms; (b) different processes to simplify the learned system; (c) tuning only specific parameters of the system; (d) the ability to tune hierarchical fuzzy systems, systems with continuous output (like fuzzy controller) as well as with categorical output (like fuzzy classifiers), and even systems that employ user-defined fuzzy functions; and, finally, (e) the ability to employ this tuning within the design flow of a fuzzy system, because xfsl is integrated into the fuzzy system development environment Xfuzzy 3.0.
{"title":"Tuning complex fuzzy systems by supervised learning algorithms","authors":"F. J. Moreno-Velo, I. Baturone, R. Senhadji, S. Sánchez-Solano","doi":"10.1109/FUZZ.2003.1209366","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209366","url":null,"abstract":"Tuning a fuzzy system to meet a given set of input/output patterns is usually a difficult task that involves many parameters. This paper presents an study of different approaches that can be applied to perform this tuning process automatically, and describes a CAD tool, named xfsl, which allows applying a wide set of these approaches: (a) a large number of supervised learning algorithms; (b) different processes to simplify the learned system; (c) tuning only specific parameters of the system; (d) the ability to tune hierarchical fuzzy systems, systems with continuous output (like fuzzy controller) as well as with categorical output (like fuzzy classifiers), and even systems that employ user-defined fuzzy functions; and, finally, (e) the ability to employ this tuning within the design flow of a fuzzy system, because xfsl is integrated into the fuzzy system development environment Xfuzzy 3.0.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116761036","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209346
A. Suratgar, S. Nikravesh
This paper presents two new approaches for linguistic modeling which are suitable for stability analysis of linguistic models. The first approach, which is named called Infinite Place model, is described by modified fuzzy Petri net and uses a new place definition based on physical infinity state. This method has some practical difficulties. In order to overcome practical difficulties, variation model is presented. The paper presents some definitions and a necessary and sufficient condition for linguistic fuzzy system stability. This stability analysis method is verified using a benchmark network simulation.
{"title":"Stability analysis of variation model for linguistic fuzzy modeling","authors":"A. Suratgar, S. Nikravesh","doi":"10.1109/FUZZ.2003.1209346","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209346","url":null,"abstract":"This paper presents two new approaches for linguistic modeling which are suitable for stability analysis of linguistic models. The first approach, which is named called Infinite Place model, is described by modified fuzzy Petri net and uses a new place definition based on physical infinity state. This method has some practical difficulties. In order to overcome practical difficulties, variation model is presented. The paper presents some definitions and a necessary and sufficient condition for linguistic fuzzy system stability. This stability analysis method is verified using a benchmark network simulation.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125292680","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209446
L. Mazlack
Causality occupies a position of centrality in human reasoning. It plays an essential role in commonsense human decision-making. Determining causes has been a tantalizing goal throughout human history. Proper sacrifices to the gods were thought to bring rewards; failure to make the proper observations to led to disaster. Today, data mining holds the promise of extracting unsuspected information from very large databases. The most common methods build association rules. In many ways, the interest in association rules is that they offer the promise (or illusion) of causal, or at least, predictive relationships. However, association rules only calculate a joint occurrence frequency; they do not express a causal relationship. If causal relationships could be discovered, it would be very useful. This paper explores the possible representation of causality drawn from large data sets.
{"title":"Causal possibility model structures","authors":"L. Mazlack","doi":"10.1109/FUZZ.2003.1209446","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209446","url":null,"abstract":"Causality occupies a position of centrality in human reasoning. It plays an essential role in commonsense human decision-making. Determining causes has been a tantalizing goal throughout human history. Proper sacrifices to the gods were thought to bring rewards; failure to make the proper observations to led to disaster. Today, data mining holds the promise of extracting unsuspected information from very large databases. The most common methods build association rules. In many ways, the interest in association rules is that they offer the promise (or illusion) of causal, or at least, predictive relationships. However, association rules only calculate a joint occurrence frequency; they do not express a causal relationship. If causal relationships could be discovered, it would be very useful. This paper explores the possible representation of causality drawn from large data sets.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122648535","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209338
D. Meng, Xiaoping Qiu
In the present paper, resolution-based automated reasoning theory and algorithm in a finite chain lattice-valued proposition logic are focused. Concretely, the resolution principle, which is based on a finite chain lattice-valued propositional logic FCLP(X) is investigated. And soundness theorem and completeness theorem of this resolution principle are also proved. In order to realize resolution, the concrete algorithm of resolution is discussed. It is hoped that this research will make forward theoretical research of automated reasoning based on lattice-valued logic.
{"title":"Resolution principle based on finite chain lattice-valued proposition logic FCLP(X)","authors":"D. Meng, Xiaoping Qiu","doi":"10.1109/FUZZ.2003.1209338","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209338","url":null,"abstract":"In the present paper, resolution-based automated reasoning theory and algorithm in a finite chain lattice-valued proposition logic are focused. Concretely, the resolution principle, which is based on a finite chain lattice-valued propositional logic FCLP(X) is investigated. And soundness theorem and completeness theorem of this resolution principle are also proved. In order to realize resolution, the concrete algorithm of resolution is discussed. It is hoped that this research will make forward theoretical research of automated reasoning based on lattice-valued logic.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122880286","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209361
D. Nauck
The "unique selling point" of fuzzy systems is usually the interpretability of its rule base. However, very often only the accuracy of the rule base is measured and used to compare a fuzzy system to other solutions. We have suggested an index to measure the interpretability of fuzzy rule bases for classification problems. However, the index can be used to describe the interpretability of any rule-based system that uses sets to partition variables. We demonstrate the features of the index by using two data sets, one simple benchmark set and a real-world example.
{"title":"Measuring interpretability in rule-based classification systems","authors":"D. Nauck","doi":"10.1109/FUZZ.2003.1209361","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209361","url":null,"abstract":"The \"unique selling point\" of fuzzy systems is usually the interpretability of its rule base. However, very often only the accuracy of the rule base is measured and used to compare a fuzzy system to other solutions. We have suggested an index to measure the interpretability of fuzzy rule bases for classification problems. However, the index can be used to describe the interpretability of any rule-based system that uses sets to partition variables. We demonstrate the features of the index by using two data sets, one simple benchmark set and a real-world example.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131590079","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1206589
T. Tomiyama, R. Ohgaya, Akiyoshi Shimmura, T. Kawabata, T. Takagi, M. Nikravesh
The objective of this paper is to develop an intelligent computer system with some deductive capabilities to conceptually cluster, match and rank pages based on predefined linguistic formulations and rules defined by experts or based on a set of known homepages. The conceptual fuzzy set (CFS) model will be used for intelligent information and knowledge retrieval through conceptual matching of both text and links (here defined as "Concept"). The selected query doesn't need to match the decision criteria exactly, which gives the system a more human-like behavior. The model can be used for intelligent information and knowledge retrieval through Web-connectivity-based clustering.
{"title":"Concept-based Web communities for Google/spl trade/ search engine","authors":"T. Tomiyama, R. Ohgaya, Akiyoshi Shimmura, T. Kawabata, T. Takagi, M. Nikravesh","doi":"10.1109/FUZZ.2003.1206589","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206589","url":null,"abstract":"The objective of this paper is to develop an intelligent computer system with some deductive capabilities to conceptually cluster, match and rank pages based on predefined linguistic formulations and rules defined by experts or based on a set of known homepages. The conceptual fuzzy set (CFS) model will be used for intelligent information and knowledge retrieval through conceptual matching of both text and links (here defined as \"Concept\"). The selected query doesn't need to match the decision criteria exactly, which gives the system a more human-like behavior. The model can be used for intelligent information and knowledge retrieval through Web-connectivity-based clustering.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121777381","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1206560
T. Wang, J. Keller, G. Cibis
Screening infants for eye problems is loaded with uncertainty. Babies are unable to describe symptoms and in general may not be cooperative. In an ongoing research project, we are developing methods to screen infants for amblyopia, a common, but treatable, eye problem. The approach consists of processing a sequence of digital frames of the baby, searching for the few images where the infant "fixes" on a light positioned by the camera. Measurements made on the detected pupils are used to produce fuzzy confidence values that are fused together to create an overall confidence of fixation (the key factor in determining amblyopia). One of the most important and difficult factors in this calculation is the determination of the Hirschberg points - points of reflection of the light source off the front of the eye- if they exist at all. The criteria for detection are best thought of as fuzzy rules and methods to score potential Hirschberg points are developed. Results are shown on a variety of imagery collected in a clinical setting.
{"title":"A fuzzy approach to find Hirschberg points and to determine fixation in digital images of infants","authors":"T. Wang, J. Keller, G. Cibis","doi":"10.1109/FUZZ.2003.1206560","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206560","url":null,"abstract":"Screening infants for eye problems is loaded with uncertainty. Babies are unable to describe symptoms and in general may not be cooperative. In an ongoing research project, we are developing methods to screen infants for amblyopia, a common, but treatable, eye problem. The approach consists of processing a sequence of digital frames of the baby, searching for the few images where the infant \"fixes\" on a light positioned by the camera. Measurements made on the detected pupils are used to produce fuzzy confidence values that are fused together to create an overall confidence of fixation (the key factor in determining amblyopia). One of the most important and difficult factors in this calculation is the determination of the Hirschberg points - points of reflection of the light source off the front of the eye- if they exist at all. The criteria for detection are best thought of as fuzzy rules and methods to score potential Hirschberg points are developed. Results are shown on a variety of imagery collected in a clinical setting.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"2019 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121790222","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209447
T. Nakashima, G. Nakai, H. Ishibuchi
This paper proposes a boosting algorithm of fuzzy rule-based systems for pattern classification problems. In the proposed algorithm, several fuzzy rule-based classification systems are incrementally constructed from a small number of training patterns. A subset of training patterns for constructing a fuzzy rule-based classification system is chosen according to weights associated to them. The weight for a training pattern is high when it is correctly classified many times. On the other hand, a low weight is assigned to those training patterns that are misclassified many times. Training patterns with a low weight are included in a subset of training patterns for constructing a single fuzzy rule-based classification system. We select the same number of training patterns from each class so that the bias in the number of training patterns among different classes is minimized. In computer simulations, we examine the performance of the boosting algorithm for the fuzzy rule-based classification systems on several real-world pattern classification problems.
{"title":"A boosting algorithm with subset selection of training patterns","authors":"T. Nakashima, G. Nakai, H. Ishibuchi","doi":"10.1109/FUZZ.2003.1209447","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209447","url":null,"abstract":"This paper proposes a boosting algorithm of fuzzy rule-based systems for pattern classification problems. In the proposed algorithm, several fuzzy rule-based classification systems are incrementally constructed from a small number of training patterns. A subset of training patterns for constructing a fuzzy rule-based classification system is chosen according to weights associated to them. The weight for a training pattern is high when it is correctly classified many times. On the other hand, a low weight is assigned to those training patterns that are misclassified many times. Training patterns with a low weight are included in a subset of training patterns for constructing a single fuzzy rule-based classification system. We select the same number of training patterns from each class so that the bias in the number of training patterns among different classes is minimized. In computer simulations, we examine the performance of the boosting algorithm for the fuzzy rule-based classification systems on several real-world pattern classification problems.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133975519","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1206623
G. Feng
This paper presents a controller design method for fuzzy dynamic systems based on techniques of piecewise smooth Lyapunov functions and bilinear matrix inequalities. The basic idea of the proposed approaches is to construct the controller for the fuzzy dynamic systems in such a way that a piecewise continuous Lyapunov function can be used to establish the global stability of the resulting closed loop fuzzy control systems. It is shown that the control law can be obtained by solving a set of Bilinear Matrix Inequalities (BMI). An example is given to illustrate the application of the proposed method.
{"title":"Controller synthesis of fuzzy dynamic systems based on piecewise Lyapunov functions and bilinear matrix inequalities","authors":"G. Feng","doi":"10.1109/FUZZ.2003.1206623","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206623","url":null,"abstract":"This paper presents a controller design method for fuzzy dynamic systems based on techniques of piecewise smooth Lyapunov functions and bilinear matrix inequalities. The basic idea of the proposed approaches is to construct the controller for the fuzzy dynamic systems in such a way that a piecewise continuous Lyapunov function can be used to establish the global stability of the resulting closed loop fuzzy control systems. It is shown that the control law can be obtained by solving a set of Bilinear Matrix Inequalities (BMI). An example is given to illustrate the application of the proposed method.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134554072","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1206549
F. Rhee, Byung-In Choi
In this paper, we propose a fast and reliable distance measure between two convex clusters using support vector machines (SVM). In doing so, the optimal hyperplane obtained by the SVM is used to calculate the minimal distance between the two clusters. As a result, an effective cluster merging algorithm that groups convex clusters resulted from the fuzzy convex clustering (FCC) method in is developed using this optimal distance. Hence, the number of clusters can be further reduced without losing its representation of the data. Several experimental results are given.
{"title":"A convex cluster merging algorithm using support vector machines","authors":"F. Rhee, Byung-In Choi","doi":"10.1109/FUZZ.2003.1206549","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206549","url":null,"abstract":"In this paper, we propose a fast and reliable distance measure between two convex clusters using support vector machines (SVM). In doing so, the optimal hyperplane obtained by the SVM is used to calculate the minimal distance between the two clusters. As a result, an effective cluster merging algorithm that groups convex clusters resulted from the fuzzy convex clustering (FCC) method in is developed using this optimal distance. Hence, the number of clusters can be further reduced without losing its representation of the data. Several experimental results are given.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"263 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134571611","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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