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.2003.1206600
B. L. Saux, Nizar Grira, N. Boujemaa
To allow efficient browsing of large image collection, we have to provide a summary of its visual content. We present in this paper a new robust approach to categorize image databases: Adaptive Robust Competition with Proximity-Based Merging (ARC-M). This algorithm relies on a non-supervised database categorization, coupled with a selection of prototypes in each resulting category. Each image is represented by a high-dimensional vector in the feature space. A principal component analysis is performed for every feature to reduce dimensionality. Then, clustering is performed in challenging conditions by minimizing a Competitive Agglomeration objective function with an extra noise cluster to collect outliers. Agglomeration is improved by a merging process based on cluster proximity verification.
{"title":"Adaptive robust clustering with proximity-based merging for video-summary","authors":"B. L. Saux, Nizar Grira, N. Boujemaa","doi":"10.1109/FUZZ.2003.1206600","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206600","url":null,"abstract":"To allow efficient browsing of large image collection, we have to provide a summary of its visual content. We present in this paper a new robust approach to categorize image databases: Adaptive Robust Competition with Proximity-Based Merging (ARC-M). This algorithm relies on a non-supervised database categorization, coupled with a selection of prototypes in each resulting category. Each image is represented by a high-dimensional vector in the feature space. A principal component analysis is performed for every feature to reduce dimensionality. Then, clustering is performed in challenging conditions by minimizing a Competitive Agglomeration objective function with an extra noise cluster to collect outliers. Agglomeration is improved by a merging process based on cluster proximity verification.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"25 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":"132009170","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.2003.1206611
H. Kargupta, Kun Liu, Souptik Datta, Jessica Ryan, K. Sivakumar
Defending the safety of an open society from terrorism or other similar threats requires intelligent but careful ways to monitor different types of activities and transactions in the electronic media. Data mining techniques are playing an increasingly important role in sifting through large amount of data in search of useful patterns that might help us in securing our safety. Although the objective of this class of data mining applications is very well justified, they also open up the possibility of misusing personal information by malicious people with access to the sensitive data. This brings up the following question: Can we design data mining techniques that are sensitive to privacy? Several researchers are currently working on a class of data mining algorithms that work without directly accessing the sensitive data in their original form. This paper considers the problem of mining distributed data in a privacy-sensitive manner. It first points out the problems of some of the existing privacy-sensitive data mining techniques that make use of additive random noise to hide sensitive information. Next it briefly reviews some new approaches that make use of random projection matrices for computing statistical aggregates from sensitive data.
{"title":"Homeland security and privacy sensitive data mining from multi-party distributed resources","authors":"H. Kargupta, Kun Liu, Souptik Datta, Jessica Ryan, K. Sivakumar","doi":"10.1109/FUZZ.2003.1206611","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206611","url":null,"abstract":"Defending the safety of an open society from terrorism or other similar threats requires intelligent but careful ways to monitor different types of activities and transactions in the electronic media. Data mining techniques are playing an increasingly important role in sifting through large amount of data in search of useful patterns that might help us in securing our safety. Although the objective of this class of data mining applications is very well justified, they also open up the possibility of misusing personal information by malicious people with access to the sensitive data. This brings up the following question: Can we design data mining techniques that are sensitive to privacy? Several researchers are currently working on a class of data mining algorithms that work without directly accessing the sensitive data in their original form. This paper considers the problem of mining distributed data in a privacy-sensitive manner. It first points out the problems of some of the existing privacy-sensitive data mining techniques that make use of additive random noise to hide sensitive information. Next it briefly reviews some new approaches that make use of random projection matrices for computing statistical aggregates from sensitive data.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"58 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":"134469610","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.2003.1209399
Zenglian Liu, C. Su, J. Svoboda
Wing rock is a highly nonlinear phenomenon in which the aircraft undergoes limit-cycle roll oscillations at high angles of attack (AOA). In this paper, a simple fuzzy PD control method is employed for wing-rock suppression and tracking because fuzzy PD controller has the same performance as the conventional PD controller for linear processes, yet improves the control capability for nonlinear and uncertain processes. Simulations at various initial conditions and different AOAs demonstrate the effectiveness and robustness of the proposed scheme. Comparison with other fuzzy PD controllers in literatures is also conducted. It shows that the proposed fuzzy controller can control wing-rock with complete and fast control effect in a wide range of AOA.
{"title":"Control of wing rock using fuzzy PD controller","authors":"Zenglian Liu, C. Su, J. Svoboda","doi":"10.1109/FUZZ.2003.1209399","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209399","url":null,"abstract":"Wing rock is a highly nonlinear phenomenon in which the aircraft undergoes limit-cycle roll oscillations at high angles of attack (AOA). In this paper, a simple fuzzy PD control method is employed for wing-rock suppression and tracking because fuzzy PD controller has the same performance as the conventional PD controller for linear processes, yet improves the control capability for nonlinear and uncertain processes. Simulations at various initial conditions and different AOAs demonstrate the effectiveness and robustness of the proposed scheme. Comparison with other fuzzy PD controllers in literatures is also conducted. It shows that the proposed fuzzy controller can control wing-rock with complete and fast control effect in a wide range of AOA.","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":"134553820","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.2003.1206636
Zhi-Qiang Liu, Yajun Zhang
In this paper we present a neural-fuzzy approach to rule extraction, which is based on a generic definition of incremental perceptron and a new competitive learning algorithm we recently developed. It extracts a suitable number of rule patches and their positions and shapes in the input space. Initially the rule base consists of only a single fuzzy rule; during the iterative learning process the rule base expands according to a supervised spawning-validity measure. The rule induction process terminates when a stop criterion is satisfied. The proposed approach will be effective in dynamic data-mining applications. To demonstrate the effectiveness and applicability of our algorithm, we present a simulation result. This algorithm is currently being tested on a number of data sets from biology and the Web.
{"title":"Rule extraction using a neuro-fuzzy learning algorithm","authors":"Zhi-Qiang Liu, Yajun Zhang","doi":"10.1109/FUZZ.2003.1206636","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206636","url":null,"abstract":"In this paper we present a neural-fuzzy approach to rule extraction, which is based on a generic definition of incremental perceptron and a new competitive learning algorithm we recently developed. It extracts a suitable number of rule patches and their positions and shapes in the input space. Initially the rule base consists of only a single fuzzy rule; during the iterative learning process the rule base expands according to a supervised spawning-validity measure. The rule induction process terminates when a stop criterion is satisfied. The proposed approach will be effective in dynamic data-mining applications. To demonstrate the effectiveness and applicability of our algorithm, we present a simulation result. This algorithm is currently being tested on a number of data sets from biology and the Web.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"62 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":"122852781","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.2003.1206546
J. Bezdek, R. Hathaway
Conventional cluster validity techniques usually represent all the validity information available about a particular clustering by a single number. The display method introduced here is an alternative to standard validity functionals. The proposed approach uses intensity images generated from the results of any prototype generator clustering algorithm as a means for cluster validation. Several numerical examples are given to illustrate the method.
{"title":"Visual cluster validity (VCV) displays for prototype generator clustering methods","authors":"J. Bezdek, R. Hathaway","doi":"10.1109/FUZZ.2003.1206546","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206546","url":null,"abstract":"Conventional cluster validity techniques usually represent all the validity information available about a particular clustering by a single number. The display method introduced here is an alternative to standard validity functionals. The proposed approach uses intensity images generated from the results of any prototype generator clustering algorithm as a means for cluster validation. Several numerical examples are given to illustrate the method.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"10 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":"121401506","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.2003.1209436
Hongwei Wu, J. Mendel
We examine ten antecedent connector models in the framework of a singleton or non-singleton fuzzy logic system (FLS) to establish which models can be used. In this work a usable connector model must lead to a separable firing degree that is a closed-form and piecewise-differentiable function of the membership function (MF) parameters and also the parameter characterizing that connector model. The. multiplicative compensatory and model that uses the product t-norm and maximum t-conorm, /spl Phi//sub p//sup MCA/, is shown to be usable for both singleton and non-singleton Mamdani-product FLSs. We also show, by examples, that the parameter of /spl Phi//sub p//sup MCA/ provides additional freedom in adjusting a FLS, so that the FLS has the potential to achieve better performance than a FLS that uses the traditional product or minimum t-norm for the antecedent connections.
{"title":"Choosing linguistic connector word models for Mamdani fuzzy logic systems","authors":"Hongwei Wu, J. Mendel","doi":"10.1109/FUZZ.2003.1209436","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209436","url":null,"abstract":"We examine ten antecedent connector models in the framework of a singleton or non-singleton fuzzy logic system (FLS) to establish which models can be used. In this work a usable connector model must lead to a separable firing degree that is a closed-form and piecewise-differentiable function of the membership function (MF) parameters and also the parameter characterizing that connector model. The. multiplicative compensatory and model that uses the product t-norm and maximum t-conorm, /spl Phi//sub p//sup MCA/, is shown to be usable for both singleton and non-singleton Mamdani-product FLSs. We also show, by examples, that the parameter of /spl Phi//sub p//sup MCA/ provides additional freedom in adjusting a FLS, so that the FLS has the potential to achieve better performance than a FLS that uses the traditional product or minimum t-norm for the antecedent connections.","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":"116318475","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.2003.1209444
Dimitar Filev, R. Yager
This paper describes a new method for automatic generation of OWA operators. It introduces a Takagi-Sugeno type model to link the process of selecting the OWA weights to the data being aggregated. A parameterized and cardinality independent type of OWA weighting vector is obtained through an analytically expression of the OWA operator as a function of the derivatives of an S-curve. These results lead to a context dependent information aggregation method.
{"title":"Context dependent information aggregation","authors":"Dimitar Filev, R. Yager","doi":"10.1109/FUZZ.2003.1209444","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209444","url":null,"abstract":"This paper describes a new method for automatic generation of OWA operators. It introduces a Takagi-Sugeno type model to link the process of selecting the OWA weights to the data being aggregated. A parameterized and cardinality independent type of OWA weighting vector is obtained through an analytically expression of the OWA operator as a function of the derivatives of an S-curve. These results lead to a context dependent information aggregation method.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"22 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":"114347575","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.2003.1206593
R.A. Resende, S. Rossi, A. Yamakami, L. H. Bonani, E. Moschim
One of the great challenges nowadays when managing IP networks is to guarantee proper Quality of Service, using network infrastructure on optimized way. One of the proposed solutions is traffic engineering with MPLS. However, the characterization of the demands and of the network state are difficult tasks, considering that the demands and the data traffic are random, consequently, the network state changes dynamically and in a random way. In this work we propose a connection admission controller that uses fuzzy logic based on linguistic rules to treat the inaccurate information in IP over MPLS networks with the purpose of offering Quality of Service to the users. In accordance with the simulation results, we concluded that the use of fuzzy logic allows a large flexibility in the connection admission process and the possibility to include more network and traffic information when making a decision without increasing considerably the controller complexity.
当前IP网络管理面临的一大挑战是如何保证适当的服务质量，优化利用网络基础设施。提出的解决方案之一是利用MPLS进行流量工程。然而，由于需求和数据流量是随机的，因此网络状态是动态随机变化的，因此需求和网络状态的表征是一项艰巨的任务。在这项工作中，我们提出了一种使用基于语言规则的模糊逻辑来处理IP over MPLS网络中的不准确信息的连接允许控制器，目的是为用户提供服务质量。根据仿真结果，我们得出结论，使用模糊逻辑可以在连接接纳过程中具有很大的灵活性，并且在做出决策时可以包含更多的网络和流量信息，而不会大大增加控制器的复杂性。
{"title":"Traffic engineering with MPLS using fuzzy logic for application in IP networks","authors":"R.A. Resende, S. Rossi, A. Yamakami, L. H. Bonani, E. Moschim","doi":"10.1109/FUZZ.2003.1206593","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206593","url":null,"abstract":"One of the great challenges nowadays when managing IP networks is to guarantee proper Quality of Service, using network infrastructure on optimized way. One of the proposed solutions is traffic engineering with MPLS. However, the characterization of the demands and of the network state are difficult tasks, considering that the demands and the data traffic are random, consequently, the network state changes dynamically and in a random way. In this work we propose a connection admission controller that uses fuzzy logic based on linguistic rules to treat the inaccurate information in IP over MPLS networks with the purpose of offering Quality of Service to the users. In accordance with the simulation results, we concluded that the use of fuzzy logic allows a large flexibility in the connection admission process and the possibility to include more network and traffic information when making a decision without increasing considerably the controller complexity.","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":"114399852","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.2003.1209380
Y. Nojima, F. Kojima, N. Kubota
This paper is concerned with a local learning method of a multi-objective behavior coordination for a mobile robot. The multiobjective behavior coordination plays a role in integrating outputs of basic behavioral modules. A behavioral weight is assigned to each behavioral module represented by fuzzy rules, production rules, and so on. By updating these behavioral weights, the mobile robot can take a multi-objective situated action. However, the coordination rule is designed suitably static environments and the mobile robot must learn or update coordination rule in dynamic environments with moving obstacles. Therefore, we propose a local episode-based learning which is a learning method using self-reference of the relationship between previous perception and action in short-term memory.
{"title":"Local episode-based learning of multi-objective behavior coordination for a mobile robot in dynamic environments","authors":"Y. Nojima, F. Kojima, N. Kubota","doi":"10.1109/FUZZ.2003.1209380","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209380","url":null,"abstract":"This paper is concerned with a local learning method of a multi-objective behavior coordination for a mobile robot. The multiobjective behavior coordination plays a role in integrating outputs of basic behavioral modules. A behavioral weight is assigned to each behavioral module represented by fuzzy rules, production rules, and so on. By updating these behavioral weights, the mobile robot can take a multi-objective situated action. However, the coordination rule is designed suitably static environments and the mobile robot must learn or update coordination rule in dynamic environments with moving obstacles. Therefore, we propose a local episode-based learning which is a learning method using self-reference of the relationship between previous perception and action in short-term memory.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"27 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":"115135672","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：