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International Conference on Fuzzy Systems最新文献

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A genetic learning of fuzzy relational rules 模糊关系规则的遗传学习
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584718
Yoel Caises, E. Leyva, A. G. Muñoz, Raúl Pérez
{"title":"A genetic learning of fuzzy relational rules","authors":"Yoel Caises, E. Leyva, A. G. Muñoz, Raúl Pérez","doi":"10.1109/FUZZY.2010.5584718","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584718","url":null,"abstract":"Two basic requirements of fuzzy modeling are the accuracy and simplicity of the knowledge obtained. In this study, we propose a genetic learning algorithm of fuzzy relational rules, that is, fuzzy rules that include fuzzy relations. Fuzzy relational rules allow us to obtain fuzzy models with a good interpretability-accuracy trade-off. Since, the inclusion of relations increases the accuracy keeping the interpretability but increasing the number of features to be considered in the learning process. We also present a model to reduce the additional complexity that occurs when using this new type of rules. Finally, we also present an experimental study that demonstrated the advantage of the use of relational fuzzy rules.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116102932","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
Study of fault feature extraction based on KPCA optimized by PSO algorithm 基于粒子群算法优化的KPCA故障特征提取研究
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5583947
Pan Hongxia, Wei Xiuye, Hu Jinying
{"title":"Study of fault feature extraction based on KPCA optimized by PSO algorithm","authors":"Pan Hongxia, Wei Xiuye, Hu Jinying","doi":"10.1109/FUZZY.2010.5583947","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5583947","url":null,"abstract":"For blindness of the parameter settings in kernel principal component analysis (KPCA), kernel function parameter optimized by particle swarm optimization (PSO) algorithm is proposed, and KPCA is applied to feature extraction in fault diagnosis. The mathematical model of kernel function parameter optimized is constructed firstly, then the PSO algorithm with adaptive accelerate (CPSO) is used to optimize it. The optimized KPCA is applied to feature extraction of gearbox typical faults. The results indicate that KPCA after parameter optimized can effectively reduce the dimensions of feature vector of gearbox, and it has a better fault classification performance than linear principal component analysis (PCA). This method has an advantage in nonlinear feature extraction of mechanical failure signal.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122023243","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: 2
An analysis of reasoning with quantifiers within the Aristotelian syllogistic framework 亚里士多德三段论框架中量词推理的分析
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584082
Martin Pereira-Fariña, F. Díaz-Hermida, Alberto Bugarín-Diz
{"title":"An analysis of reasoning with quantifiers within the Aristotelian syllogistic framework","authors":"Martin Pereira-Fariña, F. Díaz-Hermida, Alberto Bugarín-Diz","doi":"10.1109/FUZZY.2010.5584082","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584082","url":null,"abstract":"In this paper, the Aristotelian approach is proposed as an adequate framework for analyzing, categorizing and reviewing the fuzzy models for syllogistic reasoning proposed in the literature. From the analysis performed it is concluded that Zadeh and Yager's models present an incompatible structure with Aristotelian syllogistics although they can be considered as an alternative approach to syllogistic reasoning. Dubois and Prade's, nevertheless, develop an approach that is compatible with the Aristotelian framework. Although none of the revised proposals are genuinely fuzzy, the latter can be considered as an starting point to build an extension of the Aristotelian system to the fuzzy field.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122054174","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
On a fuzzy group-by clause in SQLf 关于SQLf中的模糊group-by子句
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584604
P. Bosc, O. Pivert
{"title":"On a fuzzy group-by clause in SQLf","authors":"P. Bosc, O. Pivert","doi":"10.1109/FUZZY.2010.5584604","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584604","url":null,"abstract":"Group-by is a core database operation that is used extensively in data analysis and decision support systems. In many application scenarios, it appears useful to group values according to their compliance with a certain concept instead of founding the grouping on value equality. In this paper, we propose a new SQLf construct that supports fuzzy-partition-based group-by (FGB). We show that FGB can be used to generate fuzzy summaries as well as to mine fuzzy association rules in a practical and efficient way.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122127817","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: 6
Fuzzy logic controller for freeway ramp metering with particle swarm optimization and PARAMICS simulation 基于粒子群优化和PARAMICS仿真的高速公路匝道模糊控制器
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584386
Jian-xin Xu, Xinjie Zhao, D. Srinivasan
{"title":"Fuzzy logic controller for freeway ramp metering with particle swarm optimization and PARAMICS simulation","authors":"Jian-xin Xu, Xinjie Zhao, D. Srinivasan","doi":"10.1109/FUZZY.2010.5584386","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584386","url":null,"abstract":"In this paper, two TS-type fuzzy logic controllers (FLC) in direct and incremental forms are constructed for freeway local ramp metering tasks. The values of consequent part of fuzzy rules are optimized with a particle swarm optimization algorithm (PSO). The optimization process under PSO is carried out on PARAMICS microscopic traffic simulation platform. FLC methods and the traditional ALINEA control method are examined and compared on the traffic density performance. Simulation results on PARAMICS show the applicability and efficiency of the proposed FLC.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129943373","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
Fuzzy support vector machine with a fuzzy nearest neighbor classifier for insect footprint classification 基于模糊最近邻分类器的模糊支持向量机昆虫足迹分类
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584598
Gyeongyong Heo, R. Klette, Y. Woo, Kwang-Baek Kim, Nam-Ho Kim
{"title":"Fuzzy support vector machine with a fuzzy nearest neighbor classifier for insect footprint classification","authors":"Gyeongyong Heo, R. Klette, Y. Woo, Kwang-Baek Kim, Nam-Ho Kim","doi":"10.1109/FUZZY.2010.5584598","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584598","url":null,"abstract":"The support vector machine (SVM) of statistical learning theory was successfully applied in various fields, but still suffers from noise sensitivity originating from the fact that all the data points are treated equally. To relax this problem, the SVM was extended into a fuzzy SVM (FSVM) by the introduction of fuzzy memberships. The FSVM also has been further extended in two ways, by adopting a different objective function with the help of domain-specific knowledge, or by employing a different membership calculation method. In this paper we follow the second approach by proposing a new membership calculation method using a fuzzy k nearest neighbor classifier (F-KNN). Although there are already several membership calculation methods to enhance the performance of the FSVM, one problem in those methods is that they assume a specific data distribution. The F-KNN does not assume any data distribution, which helps the proposed method to accommodate various data distributions in real world problems. The proposed algorithm was applied to an insect footprint classification problem, and results verify the effectiveness of the method.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128713606","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: 2
A fuzzy ART2 model for finding association rules in medical data 医疗数据关联规则查找的模糊ART2模型
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584780
Yo-Ping Huang, Vu Thi Thanh Hoa, Jung-Shian Jau, F. Sandnes
{"title":"A fuzzy ART2 model for finding association rules in medical data","authors":"Yo-Ping Huang, Vu Thi Thanh Hoa, Jung-Shian Jau, F. Sandnes","doi":"10.1109/FUZZY.2010.5584780","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584780","url":null,"abstract":"This paper describes a model that discovers association rules from a medical database to help doctors treat and diagnose a group of patients who show similar prehistoric medical symptoms. The proposed data mining procedure consists of two modules. The first is a clustering module that is based on a neural network, Adaptive Resonance Theory 2 (ART2), which performs affinity grouping tasks on a large amount of medical records. The other module employs fuzzy set theory to extract fuzzy association rules for each homogeneous cluster of data records. In addition, an example is given to illustrate this model. Simulation results show that the proposed algorithm can be used to obtain the desired results with a reduced processing time.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129055799","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
Intelligent user search behaviour knowledge discovery 智能用户搜索行为知识发现
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584867
Yun Shen, T. Martin
{"title":"Intelligent user search behaviour knowledge discovery","authors":"Yun Shen, T. Martin","doi":"10.1109/FUZZY.2010.5584867","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584867","url":null,"abstract":"It is important for Web search engine providers to study user behaviour in order to have a better understanding of how customers interact with search engines so that they can improve users' overall search experience. However, user behaviour in a search engine is complicated and affected by various factors, e.g. query length, intention/context/time when queries are submitted, etc. It is interesting to find answers to questions such as “whether loyal users are more likely to issue short length queries or medium length queries? If so, is that behaviour linked with high click through rate or is it linked with the user's previous search experience?” In this paper we argue that user behaviour should be better analysed from a subjective angle and introduce a granular analysis algorithm to intelligently extract user behaviour knowledge in a human-centric way to answer above questions. We study six variables relating to user behaviour study and demonstrate how fuzzy association rules mining based on mass assignment theory can intelligently analyse user activity patterns in a large scale Web search log data set.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129136150","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: 1
SYLPH: An Ambient Intelligence based platform for integrating heterogeneous Wireless Sensor Networks SYLPH:基于环境智能的集成异构无线传感器网络平台
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584145
D. I. Tapia, R. Alonso, F. D. L. Prieta, C. Zato, S. Rodríguez, E. Corchado, J. Bajo, J. Corchado
{"title":"SYLPH: An Ambient Intelligence based platform for integrating heterogeneous Wireless Sensor Networks","authors":"D. I. Tapia, R. Alonso, F. D. L. Prieta, C. Zato, S. Rodríguez, E. Corchado, J. Bajo, J. Corchado","doi":"10.1109/FUZZY.2010.5584145","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584145","url":null,"abstract":"The significance that Ambient Intelligence (AmI) has acquired in recent years requires the development of innovative solutions. Nonetheless, the development of AmI-based systems requires the creation of increasingly complex and flexible applications. In this regard, the use of context-aware technologies is an essential aspect in these developments to perceive stimuli from the context and react upon it autonomously. This work presents a novel platform that defines a method for integrating dynamic and self-adaptable heterogeneous Wireless Sensor Networks (WSNs). This approach facilitates the inclusion of context-aware capabilities when developing intelligent ubiquitous systems, where functionalities can communicate in a distributed way. Furthermore, the information obtained must be managed by intelligent and self-adaptable technologies to provide an adequate interaction between the users and their environment. Agents and Multi-Agent Systems are one of these technologies. The agents have characteristics such as autonomy, reasoning, reactivity, social abilities and pro-activity which make them appropriate for developing dynamic and distributed systems based on AmI. This way, the integration of the platform with a Service-Oriented Multi-Agent architecture is proposed. Finally, conclusions and future work are presented.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129144501","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
Cluster validity measures for data with tolerance 具有容差数据的聚类有效性度量
International Conference on Fuzzy Systems Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584371
Y. Hamasuna, Y. Endo, S. Miyamoto
{"title":"Cluster validity measures for data with tolerance","authors":"Y. Hamasuna, Y. Endo, S. Miyamoto","doi":"10.1109/FUZZY.2010.5584371","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584371","url":null,"abstract":"Cluster validity measures are used in order to determine an appropriate number of clusters and evaluate cluster partitions obtained by clustering algorithms. When we handle a set of data, data contains inherent uncertainty e.g., errors, ranges or some missing value of attributes. The concept of tolerance has been proposed from the viewpoint of handling such uncertain data. In this paper, we introduce clustering algorithms for data with tolerance. Moreover, we propose new five measures for data with tolerance, that is, the determinants and the traces of fuzzy covariance matrices, the Xie-Beni's index, the Fukuyama-Sugeno's index, and the Davies-Bouldin's index. We compare the performance of conventional ones with their tolerance versions. We found that our proposed measures takes smaller value than conventional ones. These results indicate tolerance based clustering method is suitable for handling uncertain data.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129165216","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: 1
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