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The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.最新文献

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A new genetic algorithm for nonlinear multiregressions based on generalized Choquet integrals 基于广义Choquet积分的非线性多元回归遗传算法
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03. Pub Date : 2003-05-25 DOI: 10.1109/FUZZ.2003.1206535
Zhenyuan Wang, Hai-Feng Guo
{"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}
引用次数: 43
Adaptive fuzzy segmentation of 3D MR brain images 三维磁共振脑图像的自适应模糊分割
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03. Pub Date : 2003-05-25 DOI: 10.1109/FUZZ.2003.1206564
Alan Wee-Chung Liew, Hong Yan
{"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}
引用次数: 3
Discovering reduct rules from N-indiscernibility objects in rough sets 粗糙集中n个不可分辨对象的约简规则发现
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03. Pub Date : 2003-05-25 DOI: 10.1109/FUZZ.2003.1209452
Junping Sun
{"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}
引用次数: 0
A genetic image segmentation algorithm with a fuzzy-based evaluation function 一种基于模糊评价函数的遗传图像分割算法
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03. Pub Date : 2003-05-25 DOI: 10.1109/FUZZ.2003.1206557
Xiaoying Jin, C. Davis
{"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}
引用次数: 17
Rule extraction using a neuro-fuzzy learning algorithm 使用神经模糊学习算法的规则提取
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03. Pub Date : 2003-05-25 DOI: 10.1109/FUZZ.2003.1206636
Zhi-Qiang Liu, Yajun Zhang
{"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}
引用次数: 0
Visual cluster validity (VCV) displays for prototype generator clustering methods 可视化聚类有效性(VCV)显示原型生成器聚类方法
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03. Pub Date : 2003-05-25 DOI: 10.1109/FUZZ.2003.1206546
J. Bezdek, R. Hathaway
{"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}
引用次数: 9
Choosing linguistic connector word models for Mamdani fuzzy logic systems Mamdani模糊逻辑系统的语言连接词模型选择
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03. Pub Date : 2003-05-25 DOI: 10.1109/FUZZ.2003.1209436
Hongwei Wu, J. Mendel
{"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}
引用次数: 0
Context dependent information aggregation 上下文相关的信息聚合
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03. Pub Date : 2003-05-25 DOI: 10.1109/FUZZ.2003.1209444
Dimitar Filev, R. Yager
{"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}
引用次数: 7
Traffic engineering with MPLS using fuzzy logic for application in IP networks 基于模糊逻辑的MPLS流量工程在IP网络中的应用
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03. Pub Date : 2003-05-25 DOI: 10.1109/FUZZ.2003.1206593
R.A. Resende, S. Rossi, A. Yamakami, L. H. Bonani, E. Moschim
{"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}
引用次数: 5
Local episode-based learning of multi-objective behavior coordination for a mobile robot in dynamic environments 动态环境下移动机器人多目标行为协调的局部情景学习
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03. Pub Date : 2003-05-25 DOI: 10.1109/FUZZ.2003.1209380
Y. Nojima, F. Kojima, N. Kubota
{"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}
引用次数: 11
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