Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1209419
Hugang Han, S. Murakami
When using the Lyapunov synthesis approach to construct an adaptive fuzzy control system, one important way is to regard the fuzzy systems as approximators to approximate the unknown functions in the system to be controlled. Concerning the unknownness of the unknown functions, generally there are two cases: a completely unknown case, and a partly unknown case. However, most of the schemes presented so far have only focused on the former. Clearly, if an unknown function belongs to the latter, the knowledge available about the function should be utilized as much as possible in the development of the control system. In this paper, our goal is to design an adaptive fuzzy controller for a class of model following systems with uncertainties, which can correspond to the either case. Also, we propose a unique way to deal with the uncertainties, i.e., adopt a switching function with an alterable coefficient, which is tuned by adaptive law based on the tracking error.
{"title":"Adaptive fuzzy controller for a class of model following systems","authors":"Hugang Han, S. Murakami","doi":"10.1109/FUZZ.2003.1209419","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209419","url":null,"abstract":"When using the Lyapunov synthesis approach to construct an adaptive fuzzy control system, one important way is to regard the fuzzy systems as approximators to approximate the unknown functions in the system to be controlled. Concerning the unknownness of the unknown functions, generally there are two cases: a completely unknown case, and a partly unknown case. However, most of the schemes presented so far have only focused on the former. Clearly, if an unknown function belongs to the latter, the knowledge available about the function should be utilized as much as possible in the development of the control system. In this paper, our goal is to design an adaptive fuzzy controller for a class of model following systems with uncertainties, which can correspond to the either case. Also, we propose a unique way to deal with the uncertainties, i.e., adopt a switching function with an alterable coefficient, which is tuned by adaptive law based on the tracking error.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"4 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":"116930377","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.2003.1206648
Yanqing Zhang, M. Shteynberg, S. Prasad, Rajshekhar Sunderraman
Data mining has a lot of e-commerce applications. The key problem is how to find useful hidden patterns for better business applications. For these problems, granular fuzzy Web intelligence techniques are used to implement the granular fuzzy Web data mining system for available historical data of the credit company customers. Fuzzy computing and granular computing are used to design the Web fuzzy-interval data mining system that can do fuzzy-interval data clustering under uncertainty.
{"title":"Granular fuzzy Web intelligence techniques for profitable data mining","authors":"Yanqing Zhang, M. Shteynberg, S. Prasad, Rajshekhar Sunderraman","doi":"10.1109/FUZZ.2003.1206648","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206648","url":null,"abstract":"Data mining has a lot of e-commerce applications. The key problem is how to find useful hidden patterns for better business applications. For these problems, granular fuzzy Web intelligence techniques are used to implement the granular fuzzy Web data mining system for available historical data of the credit company customers. Fuzzy computing and granular computing are used to design the Web fuzzy-interval data mining system that can do fuzzy-interval data clustering under uncertainty.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"104 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":"124828829","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.2003.1209405
C. Tseng, Bor‐Sen Chen, Bore-Kuen Lee
In general, it is a difficult work to design an efficient filter for nonlinear systems. This paper studies fuzzy filtering design for nonlinear discrete-time systems. First, the Takagi and Sugeno fuzzy model is proposed to approximate a nonlinear discrete-time system. Next, based on the fuzzy model, the fuzzy estimation for nonlinear discrete-time systems is studied. Using a suboptimal approach, the minimum variance fuzzy estimation problems are characterized in terms of an eigenvalue problem (EVP) by minimizing the upper bound on the variance of the estimation error. The EVP can be solved very efficiently using convex optimization techniques.
{"title":"Guaranteed-cost fuzzy filter design for a class of nonlinear discrete-time uncertain systems","authors":"C. Tseng, Bor‐Sen Chen, Bore-Kuen Lee","doi":"10.1109/FUZZ.2003.1209405","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209405","url":null,"abstract":"In general, it is a difficult work to design an efficient filter for nonlinear systems. This paper studies fuzzy filtering design for nonlinear discrete-time systems. First, the Takagi and Sugeno fuzzy model is proposed to approximate a nonlinear discrete-time system. Next, based on the fuzzy model, the fuzzy estimation for nonlinear discrete-time systems is studied. Using a suboptimal approach, the minimum variance fuzzy estimation problems are characterized in terms of an eigenvalue problem (EVP) by minimizing the upper bound on the variance of the estimation error. The EVP can be solved very efficiently using convex optimization techniques.","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":"125556279","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.2003.1206629
Dongwon Kim, Jang-Hyun Park, Gwi-Tae Park
We introduce a hybrid architecture that dwells on the ideas of fuzzy rule-based computing and an approximation scheme (SOPNN). The hybrid system is combined to get a novel heuristic approximation method. This composite structure overcomes the shortcomings of the individual methods especially it solves drawbacks of SOPNN while maintaining their desirable features. The combined method is efficient and much more accurate than either of the two individual schemes as well as other modeling methods. A three-input nonlinear static function is demonstrated for the utility of the proposed approach.
{"title":"Combination of fuzzy rule based model and self-organizing approximator technique: a new approach to nonlinear system modeling","authors":"Dongwon Kim, Jang-Hyun Park, Gwi-Tae Park","doi":"10.1109/FUZZ.2003.1206629","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206629","url":null,"abstract":"We introduce a hybrid architecture that dwells on the ideas of fuzzy rule-based computing and an approximation scheme (SOPNN). The hybrid system is combined to get a novel heuristic approximation method. This composite structure overcomes the shortcomings of the individual methods especially it solves drawbacks of SOPNN while maintaining their desirable features. The combined method is efficient and much more accurate than either of the two individual schemes as well as other modeling methods. A three-input nonlinear static function is demonstrated for the utility of the proposed approach.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"40 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":"122363640","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.2003.1209433
M. Menhaj, S. Akbari, S. Nikravesh
In this paper we propose a new guidance law based on fuzzy logic that can be successfully used for modeling and generating complicated offensive maneuver in an evader-pursuer task encounter between two highly responsive simplified dynamic systems called as object. Based on human expert's decision-making process, an AI based method is proposed to model the maneuvering. Fuzzy "if ... then ..." rules are used to represent the pursuer preferences in guiding his/her system. The rules are directly obtained from expert's knowledge. Each rule relates the desired moving directions of the pursuer to the task parameters. The control parameters of the object are computed through a mean square error scheme. A large amount of simulations are used to ensure the satisfactory performance of the model. The results show the similarity of the model output to human like maneuvers.
{"title":"Fuzzy modeling of offensive maneuver in an evader-pursuer task","authors":"M. Menhaj, S. Akbari, S. Nikravesh","doi":"10.1109/FUZZ.2003.1209433","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209433","url":null,"abstract":"In this paper we propose a new guidance law based on fuzzy logic that can be successfully used for modeling and generating complicated offensive maneuver in an evader-pursuer task encounter between two highly responsive simplified dynamic systems called as object. Based on human expert's decision-making process, an AI based method is proposed to model the maneuvering. Fuzzy \"if ... then ...\" rules are used to represent the pursuer preferences in guiding his/her system. The rules are directly obtained from expert's knowledge. Each rule relates the desired moving directions of the pursuer to the task parameters. The control parameters of the object are computed through a mean square error scheme. A large amount of simulations are used to ensure the satisfactory performance of the model. The results show the similarity of the model output to human like maneuvers.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"393 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":"122851113","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.2003.1206595
Adrian S. Barb, C. Shyu
It is widely recognized that fuzzy methods play an important role in image database retrieval, especially in the context of semantic queries. Known approaches that use crisp hierarchical semantic networks have been studied and applied to content-based image retrieval (CBIR) to narrow the gap between semantics and image features. Unfortunately, most of the studies lack the flexibility to adapt to an individual's preferences and/or to establish a general-purpose semantic network for sharing the perceptual understanding. In this paper, we propose a semantic query system for diagnostic image database retrieval that uses physician-defined linguistic variables. Users can obtain more desirable retrieval results by creating new, customized semantic terms, and by modeling a suite of membership functions to reflect their preferences. The system brings an increased versatility for image retrieval, and a great amount of possibilities for customizing the semantic terms using customized fuzzy mappings. Our unique approach provides various query methods that use the semantic terms within the domain of HRCT images of the lung and allows individual users to bring the contribution to the common knowledge base.
{"title":"Semantics modeling in diagnostic medical image databases using customized fuzzy membership functions","authors":"Adrian S. Barb, C. Shyu","doi":"10.1109/FUZZ.2003.1206595","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206595","url":null,"abstract":"It is widely recognized that fuzzy methods play an important role in image database retrieval, especially in the context of semantic queries. Known approaches that use crisp hierarchical semantic networks have been studied and applied to content-based image retrieval (CBIR) to narrow the gap between semantics and image features. Unfortunately, most of the studies lack the flexibility to adapt to an individual's preferences and/or to establish a general-purpose semantic network for sharing the perceptual understanding. In this paper, we propose a semantic query system for diagnostic image database retrieval that uses physician-defined linguistic variables. Users can obtain more desirable retrieval results by creating new, customized semantic terms, and by modeling a suite of membership functions to reflect their preferences. The system brings an increased versatility for image retrieval, and a great amount of possibilities for customizing the semantic terms using customized fuzzy mappings. Our unique approach provides various query methods that use the semantic terms within the domain of HRCT images of the lung and allows individual users to bring the contribution to the common knowledge base.","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":"122078451","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.2003.1209385
Yian-Kui Liu, Baoding Liu
Fuzzy variable is a function from a possibility space to the real line. In this paper, two classes of fuzzy programming problems with fuzzy variable coefficients are presented. The first one is called primal fuzzy programming problem whose objective is a chance function defined by possibility measure, while the second one is called inverse fuzzy programming problem whose objective is a critical value function. Generally, the difficulties of solving the two fuzzy programming problems are different. Thus, to solve the problems effectively, we prove two main results which show solving one of the problems is equivalent to solving its counterpart.
{"title":"Primal fuzzy programming and inverse fuzzy programming problems","authors":"Yian-Kui Liu, Baoding Liu","doi":"10.1109/FUZZ.2003.1209385","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209385","url":null,"abstract":"Fuzzy variable is a function from a possibility space to the real line. In this paper, two classes of fuzzy programming problems with fuzzy variable coefficients are presented. The first one is called primal fuzzy programming problem whose objective is a chance function defined by possibility measure, while the second one is called inverse fuzzy programming problem whose objective is a critical value function. Generally, the difficulties of solving the two fuzzy programming problems are different. Thus, to solve the problems effectively, we prove two main results which show solving one of the problems is equivalent to solving its counterpart.","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":"116718986","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.2003.1209393
Bin Zhang, Linxiang Wang, Jingcheng Wang
A method is developed to combine multiresolution analysis with fuzzy system to build dynamic system model. The proposed method consists of two parts, the construction and application of model. To construct the model, the signals are decomposed respectively to form data pairs on different scale and, the data pairs are used to construct the model on different scale whose output will be reconstructed to approximate the original signal. When this method is put into use, a certain length of past signal and current signal are used to predict the model output and, at next time instance, the past signal is push forward. This is a repeated procedure. The simulation shows the method is effective.
{"title":"The research of fuzzy modeling using multiresolution analysis","authors":"Bin Zhang, Linxiang Wang, Jingcheng Wang","doi":"10.1109/FUZZ.2003.1209393","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209393","url":null,"abstract":"A method is developed to combine multiresolution analysis with fuzzy system to build dynamic system model. The proposed method consists of two parts, the construction and application of model. To construct the model, the signals are decomposed respectively to form data pairs on different scale and, the data pairs are used to construct the model on different scale whose output will be reconstructed to approximate the original signal. When this method is put into use, a certain length of past signal and current signal are used to predict the model output and, at next time instance, the past signal is push forward. This is a repeated procedure. The simulation shows the method is effective.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"12 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":"128312543","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.2003.1209407
C. Hwang
In this paper, a nonlinear stochastic system (NSS) is approximated by weighted combination of N subsystems, which are described by ARMAX model (autoregressive moving-average model with exogenous input). The approximation error between the NSS and the stochastic fuzzy-model system (SFMS) is represented by nonlinear time-varying uncertainties (NTVU) in every subsystem. In the beginning, a dead-beat to the switching surface for every nominal subsystem is designed. The total disturbance of the ith subsystem is caused by the white noise, the approximation error of SFMS, and the interaction dynamics resulting from the other subsystems. In general, it is not small. Then the H/sup /spl infin// -norm of the weighted sensitivity function between the switching surface and the total disturbance is minimized. For obtaining a better performance, a fuzzy switching control is also designed. Finally, the simulations are carried out to confirm the validity of the proposed control.
{"title":"Fuzzy linear-model-based robust control for a class of nonlinear stochastic systems","authors":"C. Hwang","doi":"10.1109/FUZZ.2003.1209407","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209407","url":null,"abstract":"In this paper, a nonlinear stochastic system (NSS) is approximated by weighted combination of N subsystems, which are described by ARMAX model (autoregressive moving-average model with exogenous input). The approximation error between the NSS and the stochastic fuzzy-model system (SFMS) is represented by nonlinear time-varying uncertainties (NTVU) in every subsystem. In the beginning, a dead-beat to the switching surface for every nominal subsystem is designed. The total disturbance of the ith subsystem is caused by the white noise, the approximation error of SFMS, and the interaction dynamics resulting from the other subsystems. In general, it is not small. Then the H/sup /spl infin// -norm of the weighted sensitivity function between the switching surface and the total disturbance is minimized. For obtaining a better performance, a fuzzy switching control is also designed. Finally, the simulations are carried out to confirm the validity of the proposed control.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"111 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":"130626913","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.2003.1209410
Robert Babuška, M. Oosterom
The design of optimal membership functions for model-based fuzzy gain-scheduled control is addressed. The antecedent membership functions in the controller are computed such that the closed-loop behavior complies with the specifications over the entire operating range. It is shown that better performance is obtained than with the standard Parallel Distributed Control (PDC) approach, which is based on using the model membership functions in the controller. A real-world application example of aircraft gain-scheduled control is presented.
{"title":"Design of optimal membership functions for fuzzy gain-scheduled control","authors":"Robert Babuška, M. Oosterom","doi":"10.1109/FUZZ.2003.1209410","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209410","url":null,"abstract":"The design of optimal membership functions for model-based fuzzy gain-scheduled control is addressed. The antecedent membership functions in the controller are computed such that the closed-loop behavior complies with the specifications over the entire operating range. It is shown that better performance is obtained than with the standard Parallel Distributed Control (PDC) approach, which is based on using the model membership functions in the controller. A real-world application example of aircraft gain-scheduled control is presented.","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":"121230936","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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