Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1206568
M. Murakami, N. Honda, J. Nishino
This paper presents a hardware system that implements the active learning method (ALM), a methodology of soft computing. ALM has processing engines called IDS, which are tasked with extracting useful information from a system subject to modeling. In realizing ALM in hardware, it will be desirable in terms of processing nature, performance, and scalability to utilize dedicated hardware for IDS. This paper primarily describes the actual hardware design of an IDS module, and shows the findings of tests of an ALM hardware system that implemented this module.
{"title":"A hardware design for a new learning system based on fuzzy concepts","authors":"M. Murakami, N. Honda, J. Nishino","doi":"10.1109/FUZZ.2003.1206568","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206568","url":null,"abstract":"This paper presents a hardware system that implements the active learning method (ALM), a methodology of soft computing. ALM has processing engines called IDS, which are tasked with extracting useful information from a system subject to modeling. In realizing ALM in hardware, it will be desirable in terms of processing nature, performance, and scalability to utilize dedicated hardware for IDS. This paper primarily describes the actual hardware design of an IDS module, and shows the findings of tests of an ALM hardware system that implemented this module.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"98 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":"124357974","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.2003.1209452
Junping Sun
In rough set theory, the reduct is defined as a minimal set of attributes that partitions the tuple space and is used to perform the classification to achieve the equivalent result as using the whole set of attributes in a decision table. This paper is to present an incremental partitioning algorithm to discover decision rules with minimal set of attributes from rough set data. Besides developing the heuristic algorithm for discovering rules in rough sets, this paper analyzes the time complexity of the algorithm, and presents the lower bound, upper bound, and average cost of the algorithm. This paper also finds the characteristics that the lower bound and upper bound of the algorithm presented in this paper are closely related to cardinalities of attribute values from set of attributes involved in a decision table.
{"title":"Discovering reduct rules from N-indiscernibility objects in rough sets","authors":"Junping Sun","doi":"10.1109/FUZZ.2003.1209452","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209452","url":null,"abstract":"In rough set theory, the reduct is defined as a minimal set of attributes that partitions the tuple space and is used to perform the classification to achieve the equivalent result as using the whole set of attributes in a decision table. This paper is to present an incremental partitioning algorithm to discover decision rules with minimal set of attributes from rough set data. Besides developing the heuristic algorithm for discovering rules in rough sets, this paper analyzes the time complexity of the algorithm, and presents the lower bound, upper bound, and average cost of the algorithm. This paper also finds the characteristics that the lower bound and upper bound of the algorithm presented in this paper are closely related to cardinalities of attribute values from set of attributes involved in a decision table.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"1 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":"127513038","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.2003.1206557
Xiaoying Jin, C. Davis
In this paper, a genetic-based image segmentation method is proposed which optimizes a fuzzy-set-based evaluation function. A K-Means clustering method is used to generate the initial finely segmented image and to reduce the search space of the image segmentation. A genetic algorithm is then employed to control region splitting and merging to optimize the evaluation function. A critical factor affecting the performance of the segmentation is the choice of the evaluation function in the design of genetic algorithm. Here an evaluation function is defined that incorporates both edge and region information. Considering the edge ambiguity in the image, a novel fuzzy-set-based edge-boundary-coincidence measure is defined and combined with a region heterogeneity measure to guide the genetic algorithm to tune the segmentation. Experimental results on test images show that the genetic segmentation algorithm with the fuzzy-set-based evaluation function performs very well.
{"title":"A genetic image segmentation algorithm with a fuzzy-based evaluation function","authors":"Xiaoying Jin, C. Davis","doi":"10.1109/FUZZ.2003.1206557","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206557","url":null,"abstract":"In this paper, a genetic-based image segmentation method is proposed which optimizes a fuzzy-set-based evaluation function. A K-Means clustering method is used to generate the initial finely segmented image and to reduce the search space of the image segmentation. A genetic algorithm is then employed to control region splitting and merging to optimize the evaluation function. A critical factor affecting the performance of the segmentation is the choice of the evaluation function in the design of genetic algorithm. Here an evaluation function is defined that incorporates both edge and region information. Considering the edge ambiguity in the image, a novel fuzzy-set-based edge-boundary-coincidence measure is defined and combined with a region heterogeneity measure to guide the genetic algorithm to tune the segmentation. Experimental results on test images show that the genetic segmentation algorithm with the fuzzy-set-based evaluation function performs very well.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"42 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":"132621884","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.2003.1209350
Ashwani Kumar, D. P. Agrawal, S. Joshi
A method based on genetic algorithm (GA), a simple clustering procedure for rule base generation, and weighted least squares estimation is proposed to construct a Takagi-Sugeno-Kang (TSK) fuzzy inference system directly from numerical data. The rule-base generation method takes the approach of independently clustering input and output spaces, respectively, and assigning a weight to each rule to capture the relation in input-output data. Genetic process learns the number of linguistic terms per variable and the certainty factors of the rules (indirectly the membership-function parameters of the premise part of the fuzzy rules), and the weighted least squares method is used to determine the consequent part of the fuzzy rules. Simulation results on forecasting the stock market and a benchmark case study are included.
{"title":"A GA-based method for constructing TSK fuzzy rules from numerical data","authors":"Ashwani Kumar, D. P. Agrawal, S. Joshi","doi":"10.1109/FUZZ.2003.1209350","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209350","url":null,"abstract":"A method based on genetic algorithm (GA), a simple clustering procedure for rule base generation, and weighted least squares estimation is proposed to construct a Takagi-Sugeno-Kang (TSK) fuzzy inference system directly from numerical data. The rule-base generation method takes the approach of independently clustering input and output spaces, respectively, and assigning a weight to each rule to capture the relation in input-output data. Genetic process learns the number of linguistic terms per variable and the certainty factors of the rules (indirectly the membership-function parameters of the premise part of the fuzzy rules), and the weighted least squares method is used to determine the consequent part of the fuzzy rules. Simulation results on forecasting the stock market and a benchmark case study are included.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"17 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":"133792739","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.2003.1209431
Z. Bien, Dae-Jin Kim, Hyong-Euk Lee, Kwang-Hyun Park, Haiying She, C. Martens, A. Gräser
Human's intention plays a key role in human-machine interaction as in the case of a robot serving for a handicapped person. The quality of a service robot will be much enhanced if the robot can infer the human's intension during the interaction process. In this paper, we propose a soft computing-based technique to read a user's intention using some multisensors-based approach. We have tested the technique by a scenario of 'serving a drink to the user'. With such force/torque or vision sensor, the robot can effectively infer the user's intention to drink the beverage or not to drink. As an application, this intention technique is employed for building a rehabilitation robot, called KARES II, to perform various human-friendly human-robot interaction.
{"title":"Multi sensors-based approach for intention reading with soft computing techniques","authors":"Z. Bien, Dae-Jin Kim, Hyong-Euk Lee, Kwang-Hyun Park, Haiying She, C. Martens, A. Gräser","doi":"10.1109/FUZZ.2003.1209431","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209431","url":null,"abstract":"Human's intention plays a key role in human-machine interaction as in the case of a robot serving for a handicapped person. The quality of a service robot will be much enhanced if the robot can infer the human's intension during the interaction process. In this paper, we propose a soft computing-based technique to read a user's intention using some multisensors-based approach. We have tested the technique by a scenario of 'serving a drink to the user'. With such force/torque or vision sensor, the robot can effectively infer the user's intention to drink the beverage or not to drink. As an application, this intention technique is employed for building a rehabilitation robot, called KARES II, to perform various human-friendly human-robot interaction.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"1 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":"133523050","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.2003.1209386
F. J. Moreno-Velo, I. Baturone, S. Sánchez-Solano, A. Barriga
The crecient use of fuzzy systems in complex applications has motivated us to develop a new version of Xfuzzy, the design environment for fuzzy system created at the IMSE (Instituto de Microelectronica de Sevilla). This new version, Xfuzzy 3.0, offers the advantages of being enterely programmed in Java, and allows designing hierarchical rule bases that can interchange fuzzy or non fuzzy values as well as employ user-defined fuzzy connectives, linguistic hedges, membership functions, and defuzzification methods. Xfuzzy 3.0 integrates tools that facilitate the description, tuning, verification, and synthesis of complex fuzzy systems. This is illustrated in this paper with the design of a fuzzy controller to solve a parking problem.
{"title":"Rapid design of fuzzy systems with Xfuzzy","authors":"F. J. Moreno-Velo, I. Baturone, S. Sánchez-Solano, A. Barriga","doi":"10.1109/FUZZ.2003.1209386","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209386","url":null,"abstract":"The crecient use of fuzzy systems in complex applications has motivated us to develop a new version of Xfuzzy, the design environment for fuzzy system created at the IMSE (Instituto de Microelectronica de Sevilla). This new version, Xfuzzy 3.0, offers the advantages of being enterely programmed in Java, and allows designing hierarchical rule bases that can interchange fuzzy or non fuzzy values as well as employ user-defined fuzzy connectives, linguistic hedges, membership functions, and defuzzification methods. Xfuzzy 3.0 integrates tools that facilitate the description, tuning, verification, and synthesis of complex fuzzy systems. This is illustrated in this paper with the design of a fuzzy controller to solve a parking problem.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"16 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":"131061985","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.2003.1209437
J. Baldwin, E. D. Tomaso
This paper deals with the development of a theory on bayesian networks. It proposes a modified algorithm for solving knowledge querying and information updating, when dealing with continuous variables and with probabilistic and uncertain instantiations. Fuzzy sets are used to rewrite the information contained in a database in order to reduce the complexity of the automatic learning of a bayesian net from data.
{"title":"Inference and learning in fuzzy bayesian networks","authors":"J. Baldwin, E. D. Tomaso","doi":"10.1109/FUZZ.2003.1209437","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209437","url":null,"abstract":"This paper deals with the development of a theory on bayesian networks. It proposes a modified algorithm for solving knowledge querying and information updating, when dealing with continuous variables and with probabilistic and uncertain instantiations. Fuzzy sets are used to rewrite the information contained in a database in order to reduce the complexity of the automatic learning of a bayesian net from data.","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":"131530571","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.2003.1206592
T. Joronen
The development of fuzzy sets has led to computational theory of perceptions (CTP). This paper presents a philosophical study on fuzzy sets and fuzzy applications and aims towards a deeper understanding about them. Ludwig Wittgenstein's philosophy can be used to illustrate fuzzy sets. Relating to Wittgenstein's approach, some interesting studies on 'vagueness' appeared before the genesis of fuzzy sets in 1965. We introduce a simple meaning articulation paradigm (MAP) of human meaning processing and apply it to fuzzy applications. The MAP applied to two case studies on fuzzy optimization and on a fuzzy Web query shows that some problems exist in traditional approaches.
{"title":"A philosophical study on fuzzy sets and fuzzy applications","authors":"T. Joronen","doi":"10.1109/FUZZ.2003.1206592","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206592","url":null,"abstract":"The development of fuzzy sets has led to computational theory of perceptions (CTP). This paper presents a philosophical study on fuzzy sets and fuzzy applications and aims towards a deeper understanding about them. Ludwig Wittgenstein's philosophy can be used to illustrate fuzzy sets. Relating to Wittgenstein's approach, some interesting studies on 'vagueness' appeared before the genesis of fuzzy sets in 1965. We introduce a simple meaning articulation paradigm (MAP) of human meaning processing and apply it to fuzzy applications. The MAP applied to two case studies on fuzzy optimization and on a fuzzy Web query shows that some problems exist in traditional approaches.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"30 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":"128312601","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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