Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1206535
Zhenyuan Wang, Hai-Feng Guo
This paper gives a new genetic algorithm for nonlinear multiregression based on generalized Choquet integrals with respect to signed fuzzy measures. Unlike the previous work where the values of the signed fuzzy measure are determined by random search in a genetic algorithm with other regression coefficients together; in this new algorithm, they are determined algebraically and, therefore, its complexity is much lower than before.
{"title":"A new genetic algorithm for nonlinear multiregressions based on generalized Choquet integrals","authors":"Zhenyuan Wang, Hai-Feng Guo","doi":"10.1109/FUZZ.2003.1206535","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206535","url":null,"abstract":"This paper gives a new genetic algorithm for nonlinear multiregression based on generalized Choquet integrals with respect to signed fuzzy measures. Unlike the previous work where the values of the signed fuzzy measure are determined by random search in a genetic algorithm with other regression coefficients together; in this new algorithm, they are determined algebraically and, therefore, its complexity is much lower than before.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"67 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":"126265529","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.2003.1206564
Alan Wee-Chung Liew, Hong Yan
A fuzzy c-means based adaptive clustering algorithm is proposed for the fuzzy segmentation of 3D MR brain images, which are typically corrupted by noise and intensity non-uniformity (INU) artifact. The proposed algorithm enforces the spatial continuity constraint to account for the spatial correlations between image voxels, resulting in the suppression of noise and classification ambiguity. The INU artifact is compensated for by the introduction of a pseudo-3D bias field, which is modeled as a stack of smooth B-spline surfaces with continuity enforced across slices. The efficacy of the proposed algorithm is demonstrated experimentally using both simulated and real MR images.
{"title":"Adaptive fuzzy segmentation of 3D MR brain images","authors":"Alan Wee-Chung Liew, Hong Yan","doi":"10.1109/FUZZ.2003.1206564","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206564","url":null,"abstract":"A fuzzy c-means based adaptive clustering algorithm is proposed for the fuzzy segmentation of 3D MR brain images, which are typically corrupted by noise and intensity non-uniformity (INU) artifact. The proposed algorithm enforces the spatial continuity constraint to account for the spatial correlations between image voxels, resulting in the suppression of noise and classification ambiguity. The INU artifact is compensated for by the introduction of a pseudo-3D bias field, which is modeled as a stack of smooth B-spline surfaces with continuity enforced across slices. The efficacy of the proposed algorithm is demonstrated experimentally using both simulated and real MR images.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"445 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":"123590320","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.2003.1206636
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}
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