Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1209454
Jiwen Chen, Jianhua Chen, G. Kemp
In this paper, a hybrid decision tree learning approach is presented that combines fuzzy C-means method and the ID3 algorithm in decision tree construction from continuous-valued features. The fuzzy C-means method is applied to find a number of central means for each continuous-valued feature and thus discretize such features. The ID3 algorithm is subsequently used to build a decision tree from the discretized data. Preliminary experiments using a real-world time-series data set from the Louisiana coast are reported that compare our method with the OC1 system for oblique decision tree learning. The experiment results seem to suggest that the proposed hybrid method achieves better or comparable classification accuracy.
{"title":"Fuzzy clustering and decision tree learning for time-series tidal data classification","authors":"Jiwen Chen, Jianhua Chen, G. Kemp","doi":"10.1109/FUZZ.2003.1209454","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209454","url":null,"abstract":"In this paper, a hybrid decision tree learning approach is presented that combines fuzzy C-means method and the ID3 algorithm in decision tree construction from continuous-valued features. The fuzzy C-means method is applied to find a number of central means for each continuous-valued feature and thus discretize such features. The ID3 algorithm is subsequently used to build a decision tree from the discretized data. Preliminary experiments using a real-world time-series data set from the Louisiana coast are reported that compare our method with the OC1 system for oblique decision tree learning. The experiment results seem to suggest that the proposed hybrid method achieves better or comparable classification accuracy.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"106 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":"117300471","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.2003.1209358
C. Aouiti, A. Alimi, F. Karray, A. Maalej
We propose an evolutionary method for the design of Beta fuzzy systems (BFS). Classical training algorithms start with a predetermined number of fuzzy rules for fuzzy systems. Generally speaking, the fuzzy system created is either insufficient or over-complicated. This paper describes a hierarchical genetic learning model of the BFS. In order to examine the performance of the proposed algorithm, it is used for the identification of an induction machine fuzzy plant model. The results obtained have been encouraging.
{"title":"Evolutionary approach for the beta function based fuzzy systems","authors":"C. Aouiti, A. Alimi, F. Karray, A. Maalej","doi":"10.1109/FUZZ.2003.1209358","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209358","url":null,"abstract":"We propose an evolutionary method for the design of Beta fuzzy systems (BFS). Classical training algorithms start with a predetermined number of fuzzy rules for fuzzy systems. Generally speaking, the fuzzy system created is either insufficient or over-complicated. This paper describes a hierarchical genetic learning model of the BFS. In order to examine the performance of the proposed algorithm, it is used for the identification of an induction machine fuzzy plant model. The results obtained have been encouraging.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"93 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":"117302063","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.2003.1209426
S. Aja‐Fernández, C. Alberola-López
In this paper we propose an alternative implementation of the concept of fuzzy granule. Granules are defined in terms of the degree of overlap with other granules, as opposed to by assigning (somehow arbitrarily) membership values to each and every point of the university of discourse in which the granule is defined. We believe this alternative definition is much closer to the human way of thinking. Two examples of real world applications illustrate this new definition.
{"title":"Inference with fuzzy granules for computing with words: a practical viewpoint","authors":"S. Aja‐Fernández, C. Alberola-López","doi":"10.1109/FUZZ.2003.1209426","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209426","url":null,"abstract":"In this paper we propose an alternative implementation of the concept of fuzzy granule. Granules are defined in terms of the degree of overlap with other granules, as opposed to by assigning (somehow arbitrarily) membership values to each and every point of the university of discourse in which the granule is defined. We believe this alternative definition is much closer to the human way of thinking. Two examples of real world applications illustrate this new definition.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"19 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":"116651924","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.2003.1209384
U. Kaymak, W. Bergh, J. V. D. Berg
We introduce a type of probabilistic fuzzy system with a generalized Mamdani-type fuzzy rule base, and an additive reasoning scheme where conditional probabilities on fuzzy events are aggregated using an interpolation approach. In this way, probabilistic fuzzy outputs can be calculated for arbitrary crisp input vectors. If desired, the probabilistic fuzzy output can be made crisp using a defuzzification and averaging step. Besides introducing the architecture of the probabilistic fuzzy systems and the corresponding equations for calculating the input-output mapping, we summarize some key results from the probability theory and statistics on fuzzy sets. To show the working of the probabilistic fuzzy models introduced, we analyze a simulated GARCH time series using a data-driven approach. A probabilistic fuzzy rule-base is derived from the given data set containing rules that yield a rather good intuitive description of the underlying GARCH-process. Further, we show some additional results like the estimated regression plane and several (un)conditional probability distributions.
{"title":"A fuzzy additive reasoning scheme for probabilistic Mamdani fuzzy systems","authors":"U. Kaymak, W. Bergh, J. V. D. Berg","doi":"10.1109/FUZZ.2003.1209384","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209384","url":null,"abstract":"We introduce a type of probabilistic fuzzy system with a generalized Mamdani-type fuzzy rule base, and an additive reasoning scheme where conditional probabilities on fuzzy events are aggregated using an interpolation approach. In this way, probabilistic fuzzy outputs can be calculated for arbitrary crisp input vectors. If desired, the probabilistic fuzzy output can be made crisp using a defuzzification and averaging step. Besides introducing the architecture of the probabilistic fuzzy systems and the corresponding equations for calculating the input-output mapping, we summarize some key results from the probability theory and statistics on fuzzy sets. To show the working of the probabilistic fuzzy models introduced, we analyze a simulated GARCH time series using a data-driven approach. A probabilistic fuzzy rule-base is derived from the given data set containing rules that yield a rather good intuitive description of the underlying GARCH-process. Further, we show some additional results like the estimated regression plane and several (un)conditional probability distributions.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"93 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":"116681050","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.2003.1206526
R. Chau, C. Yeh
An emerging requirement to sift through the increasing flood of multilingual text available electronically has led to the pressing demand for effective multilingual information filtering. In this paper, a content-based approach to multilingual information filtering is proposed. This approach is capable of screening and evaluating multilingual documents based on their semantic content. As such, relevant multilingual documents are disseminated according to their corresponding themes/topics to facilitate both efficient and effective content-based information access. The objective of alleviating users' burden of information overload is thus achieved. This approach is realized by incorporating fuzzy clustering and fuzzy inference techniques.
{"title":"Fuzzy multilingual information filtering","authors":"R. Chau, C. Yeh","doi":"10.1109/FUZZ.2003.1206526","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206526","url":null,"abstract":"An emerging requirement to sift through the increasing flood of multilingual text available electronically has led to the pressing demand for effective multilingual information filtering. In this paper, a content-based approach to multilingual information filtering is proposed. This approach is capable of screening and evaluating multilingual documents based on their semantic content. As such, relevant multilingual documents are disseminated according to their corresponding themes/topics to facilitate both efficient and effective content-based information access. The objective of alleviating users' burden of information overload is thus achieved. This approach is realized by incorporating fuzzy clustering and fuzzy inference techniques.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"44 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":"116313009","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.2003.1209318
Zhiheng Huang, Q. Shen
Interpolative reasoning methods do not only help reduce the complexity of fuzzy models but also make inference in sparse-rule based systems possible. This paper presents an interpolative reasoning method by exploiting the center of gravity (COG) property of the fuzzy sets concerned. The method works by first constructing a new inference rule via manipulating two given adjacent rules, and then by using similarity information to convert the intermediate inference results into the final derived conclusion. Two transformation operations are introduced to support such reasoning, which allow the COG of a fuzzy set to remain unaltered before and after the transformation. Results of experimental comparisons are provided to reflect the success of this work.
{"title":"A new fuzzy interpolative reasoning method based on center of gravity","authors":"Zhiheng Huang, Q. Shen","doi":"10.1109/FUZZ.2003.1209318","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209318","url":null,"abstract":"Interpolative reasoning methods do not only help reduce the complexity of fuzzy models but also make inference in sparse-rule based systems possible. This paper presents an interpolative reasoning method by exploiting the center of gravity (COG) property of the fuzzy sets concerned. The method works by first constructing a new inference rule via manipulating two given adjacent rules, and then by using similarity information to convert the intermediate inference results into the final derived conclusion. Two transformation operations are introduced to support such reasoning, which allow the COG of a fuzzy set to remain unaltered before and after the transformation. Results of experimental comparisons are provided to reflect the success of this work.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"8 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":"125513537","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.2003.1209391
M. F. Zarandi, S. Saghiri
Supplier selection is understood as one of the key processes in strategic decision making level in Supply Chains (SC). This paper develops a comprehensive multiple products and multiple suppliers model for this process. Moreover, various targets are discussed and analyzed in the form of objectives, in addition to related constraints. Such model development is fulfilled in a real-world situation with wide ranges of uncertainties. In this paper, a fuzzy decision making model is presented. In the proposed Fuzzy Multiple Objectives Decision Making (FMODM) model, all goals, constraints, variables and coefficients are fuzzy. It is shown that with the application of the fuzzy methodology, the complex multi-objective problem is converted to a single one that can be solved and interpreted easily.
{"title":"A comprehensive fuzzy multi-objective model for supplier selection process","authors":"M. F. Zarandi, S. Saghiri","doi":"10.1109/FUZZ.2003.1209391","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209391","url":null,"abstract":"Supplier selection is understood as one of the key processes in strategic decision making level in Supply Chains (SC). This paper develops a comprehensive multiple products and multiple suppliers model for this process. Moreover, various targets are discussed and analyzed in the form of objectives, in addition to related constraints. Such model development is fulfilled in a real-world situation with wide ranges of uncertainties. In this paper, a fuzzy decision making model is presented. In the proposed Fuzzy Multiple Objectives Decision Making (FMODM) model, all goals, constraints, variables and coefficients are fuzzy. It is shown that with the application of the fuzzy methodology, the complex multi-objective problem is converted to a single one that can be solved and interpreted easily.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"19 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":"123837859","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.2003.1209412
H. Allamehzadeh, J. Cheung
A new chattering-free Sliding Mode Fuzzy Controller (SMFC) with smooth control law is proposed for a class of nonlinear system. The proposed controller employs the concept of variable structure system with sliding mode or Sliding Mode Control (SMC), for design, and preserves the most fundamental property of conventional SMC that is robustness and invariance to model uncertainties. However, unlike the conventional sliding mode control, SMFC eliminates chattering problem through the concept input-output mapping factor and behave like a linear controller in the neighborhood of its sliding manifold. To demonstrate the superiority of SMFC over SMC, we conducted simulation studies on balancing an inverted pendulum at its upright position in the presence model uncertainties and external disturbances.
{"title":"Smooth response sliding mode fuzzy control with intrinsic boundary layer","authors":"H. Allamehzadeh, J. Cheung","doi":"10.1109/FUZZ.2003.1209412","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209412","url":null,"abstract":"A new chattering-free Sliding Mode Fuzzy Controller (SMFC) with smooth control law is proposed for a class of nonlinear system. The proposed controller employs the concept of variable structure system with sliding mode or Sliding Mode Control (SMC), for design, and preserves the most fundamental property of conventional SMC that is robustness and invariance to model uncertainties. However, unlike the conventional sliding mode control, SMFC eliminates chattering problem through the concept input-output mapping factor and behave like a linear controller in the neighborhood of its sliding manifold. To demonstrate the superiority of SMFC over SMC, we conducted simulation studies on balancing an inverted pendulum at its upright position in the presence model uncertainties and external disturbances.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"2 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":"128022554","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.2003.1209334
J. Mendel
This paper begins with a delineation of two approaches to fuzzy sets, abstract mathematics and models for words. It demonstrates, by using Karl Popper's Falsificationism, the present approach to fuzzy sets (FSs) for words is scientifically incorrect. A new theory of fuzzy sets is then presented for words that is based on collecting data from people -person MFs-that reflect intra- and inter-levels of uncertainties about a word, and defines a word FS as the union of all such person fuzzy sets. It also demonstrates that intra-uncertainty about a word can be modeled using type-2 person fuzzy sets, and that inter-uncertainty about a word can be modeled by means of an equally weighted union of each person's type-2 fuzzy set. Finally, it proposes a methodology for obtaining a parsimonious parametric type-2 fuzzy set approximation to the aggregated type-2 person FSs. This new theory of fuzzy sets for words is testable and is therefore subject to refutation.
{"title":"Fuzzy sets for words: a new beginning","authors":"J. Mendel","doi":"10.1109/FUZZ.2003.1209334","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209334","url":null,"abstract":"This paper begins with a delineation of two approaches to fuzzy sets, abstract mathematics and models for words. It demonstrates, by using Karl Popper's Falsificationism, the present approach to fuzzy sets (FSs) for words is scientifically incorrect. A new theory of fuzzy sets is then presented for words that is based on collecting data from people -person MFs-that reflect intra- and inter-levels of uncertainties about a word, and defines a word FS as the union of all such person fuzzy sets. It also demonstrates that intra-uncertainty about a word can be modeled using type-2 person fuzzy sets, and that inter-uncertainty about a word can be modeled by means of an equally weighted union of each person's type-2 fuzzy set. Finally, it proposes a methodology for obtaining a parsimonious parametric type-2 fuzzy set approximation to the aggregated type-2 person FSs. This new theory of fuzzy sets for words is testable and is therefore subject to refutation.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"44 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":"128599872","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.2003.1206616
M. Graña, P. Sussner, G. Ritter
Autoassociative morphological memories (AMM) are a construct similar to hopfield autoassociatived memories defined on the (R, +, v, /spl and/) lattice algebra. Unlimited storage and perfect recall of noiseless real valued patterns has been proved for AMMs. However AMMs suffer from sensitivity to specific noise models, that can be characterized as erosive and dilative noise. On the other hand, spectral unmixing of hyperspectral images needs the prior definition of a set of endmembers, which correspond to material spectra lying on vertices of the minimum convex region covering the image data. These vertices can be characterized as morphologically independent patterns. We present a procedure based on the AMM noise sensitivity for endmember detection based on this characterization.
{"title":"Associative morphological memories for endmember determination in spectral unmixing","authors":"M. Graña, P. Sussner, G. Ritter","doi":"10.1109/FUZZ.2003.1206616","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206616","url":null,"abstract":"Autoassociative morphological memories (AMM) are a construct similar to hopfield autoassociatived memories defined on the (R, +, v, /spl and/) lattice algebra. Unlimited storage and perfect recall of noiseless real valued patterns has been proved for AMMs. However AMMs suffer from sensitivity to specific noise models, that can be characterized as erosive and dilative noise. On the other hand, spectral unmixing of hyperspectral images needs the prior definition of a set of endmembers, which correspond to material spectra lying on vertices of the minimum convex region covering the image data. These vertices can be characterized as morphologically independent patterns. We present a procedure based on the AMM noise sensitivity for endmember detection based on this characterization.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"69 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":"129559115","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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