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2007 IEEE International Fuzzy Systems Conference最新文献

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Microcalcification Detection in Mammograms Using Interval Type-2 Fuzzy Logic System 区间2型模糊逻辑系统在乳房x线微钙化检测中的应用
2007 IEEE International Fuzzy Systems Conference Pub Date : 2010-07-18 DOI: 10.1109/FUZZY.2010.5584896
Suraphon Chumklin, S. Auephanwiriyakul, N. Theera-Umpon
{"title":"Microcalcification Detection in Mammograms Using Interval Type-2 Fuzzy Logic System","authors":"Suraphon Chumklin, S. Auephanwiriyakul, N. Theera-Umpon","doi":"10.1109/FUZZY.2010.5584896","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584896","url":null,"abstract":"Breast cancer is an important deleterious disease. Mortality rate from this cancer is effectively high and rapidly increasing. The detection at the earlier state can help to reduce the mortality rate. In this paper, we develop a system that helps radiologists to detect microcalcification in mammograms. In particular, we apply the interval type-2 fuzzy logic system with four features, i.e., B-descriptor, D-descriptor, average intensity inside boundary, and intensity difference between inside and outside boundaries. We also compare the result with the result from a type-1 Mamdani fuzzy inference system with the same set of features. The result from the type-1 fuzzy logic system yields 87.95% correct classification with 11.33 false positives per image whereas interval type-2 fuzzy logic system provides 90.36% correct classification with only 4.73 false positives per image.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"50 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":"124839143","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: 14
System of fuzzy relation equations with sup-* composition in semi-linear spaces: minimal solutions 半线性空间中具有sup *组合的模糊关系方程组的最小解
2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-08-27 DOI: 10.1109/FUZZY.2007.4295592
L. Nosková, I. Perfilieva
{"title":"System of fuzzy relation equations with sup-* composition in semi-linear spaces: minimal solutions","authors":"L. Nosková, I. Perfilieva","doi":"10.1109/FUZZY.2007.4295592","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295592","url":null,"abstract":"The problem of solvability of a system of fuzzy relation equations with sup-* composition is considered in semilinear vector spaces. Based on the fact that a complete set of solutions is determined by minimal solutions, we focused on characterization of them. At first, sets of all minimal solutions of a single equation have been described under different assumptions on an underlying algebra. Dependently on the ordering of the support set, either necessary or sufficient conditions, or criteria of being a minimal solution have been obtained. Then minimal solutions of a system are build from minimal solutions of single equations.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116010653","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
Parallel Type-2 Fuzzy Logic Co-Processors for Engine Management 用于发动机管理的并行2型模糊逻辑协处理器
2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-08-27 DOI: 10.1109/FUZZY.2007.4295486
C. Lynch, H. Hagras, V. Callaghan
{"title":"Parallel Type-2 Fuzzy Logic Co-Processors for Engine Management","authors":"C. Lynch, H. Hagras, V. Callaghan","doi":"10.1109/FUZZY.2007.4295486","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295486","url":null,"abstract":"Marine diesel engines operate in highly dynamic and uncertain environments, hence they require robust and accurate speed controllers that can handle the encountered uncertainties. Type-2 fuzzy logic controllers (FLCs) have shown that they can handle such uncertainties and give a superior performance to the existing commercial controllers. However, there are a number of computational bottlenecks that pose as significant barriers to the widespread deployment of type-2 FLCs in commercial embedded control systems. This paper explores the use of parallel hardware implementations of interval type-2 FLC as a means to eradicate these barriers thus producing bespoke co-processors for a soft core implementation of a FPGA based 32 bit RISC micro-processor. These coprocessors will perform functions such as fuzzification and type reduction and are currently utilised as part of a larger embedded interval type-2 fuzzy engine management system (T2FEMS). Numerous timing comparisons were undertaken between the co-processors and their sequential counterparts where the type-2 co-processors reduced significantly the computational cycles required by the type-2 FLC. This reduction in computational cycles allowed the T2FEMS to produce faster control responses whilst offering a superior control performance to the commercial engine management systems. Thus the proposed co-processors enable us to fully explore the potential of interval and possibly general type-2 FLCs in commercial embedded applications.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134334780","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: 22
Propositional Gödel Logic and Delannoy Paths 命题Gödel逻辑和Delannoy路径
2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295542
P. Codara, O. D'Antona, V. Marra
{"title":"Propositional Gödel Logic and Delannoy Paths","authors":"P. Codara, O. D'Antona, V. Marra","doi":"10.1109/FUZZY.2007.4295542","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295542","url":null,"abstract":"Godel propositional logic is the logic of the minimum triangular norm, and can be axiomatized as propositional intuitionistic logic augmented by the prelinearity axiom (alpha rarr beta) V (beta rarr alpha). Its algebraic counterpart is the subvariety of Heyting algebras satisfying prelinearity, known as Godel algebras. A Delannoy path is a lattice path in Z2 that only uses northward, eastward, and northeastward steps. We establish a representation theorem for free n-generated Godel algebras in terms of the Boolean n-cube {0,1}n, enriched by suitably generalized Delannoy paths.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124261080","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: 4
An Optimization Approach to Fuzzy Diagnosis: Oil Analysis Application 模糊诊断的优化方法:油液分析应用
2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295582
A. Sala, J. Ramirez, B. Tormos, Manuel Yago
{"title":"An Optimization Approach to Fuzzy Diagnosis: Oil Analysis Application","authors":"A. Sala, J. Ramirez, B. Tormos, Manuel Yago","doi":"10.1109/FUZZY.2007.4295582","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295582","url":null,"abstract":"This paper discusses a knowledge-base encoding methodology for diagnostic tasks. It transform \"expert\"-provided rules into algebraic expressions so inference of the \"possible\" disorders is carried out via associated constrained optimisation problems. In this way, the need of conventional fuzzy inference systems or \"uncertain\"-logic schemes is no longer present in the particular setting in this paper. An oil-analysis diagnosis case study is presented as an application example, with actual experimental data. The problem is solved by efficient linear programming tools, in principle able to cope with large-scale problems. The only software used was Mathematica reg 5.2.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117155412","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
Real Time Knowledge Acquisition Based on Unsupervised Learning of Evolving Neural Models 基于进化神经模型无监督学习的实时知识获取
2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295560
G. Vachkov
{"title":"Real Time Knowledge Acquisition Based on Unsupervised Learning of Evolving Neural Models","authors":"G. Vachkov","doi":"10.1109/FUZZY.2007.4295560","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295560","url":null,"abstract":"This paper presents a method for extraction of knowledge from a real time process by using the so called evolving neural model (ENM). The ENM learns from real time data streams by a specially proposed evolving unsupervised learning algorithm. This algorithm is further development of the off-line neural-gas learning with a different way of updating the neurons. It also uses a special logic to prevent the neurons from gradually becoming \"idling\" during the evolutions. Two characteristics of the ENM, namely the center-of-gravity COG and the weighted average size WAS of the model are further used to capture the general trends of operation changes in the process. Big changes serve as indication for acquisition of a new knowledge about the process that should be saved in the knowledge base. Normalized data taken from different operations of a diesel engine for hydraulic excavator are used to test and verify the merits of the proposed learning algorithm and the whole knowledge acquisition method.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117172442","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 Migration Approach from Crisp Databases to Fuzzy Databases 从清晰数据库到模糊数据库的迁移方法
2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295651
M. Hassine, Habib Ounelli, A. Touzi, José Galindo
{"title":"A Migration Approach from Crisp Databases to Fuzzy Databases","authors":"M. Hassine, Habib Ounelli, A. Touzi, José Galindo","doi":"10.1109/FUZZY.2007.4295651","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295651","url":null,"abstract":"Fuzzy databases (DB) have been introduced to deal with uncertain or incomplete information in many applications demonstrating the efficiency of processing fuzzy queries. For these reasons, many organizations aim to integrate the flexible querying, to handle imprecise data or to use fuzzy data mining tools, minimizing the transformation costs. The best solution is to offer a smoothly migration toward this technology. This paper presents a new approach for the migration from relational DB (RDB) towards fuzzy relational DB (FRDB). The goal of this migration is to permit an easy mapping of the existing data, schemas and programs, while integrating the different fuzzy concepts. This paper presents two migration strategies. The first one, named \"partial migration\", is useful to introduce fuzzy queries in RDB without changing existing data. The \"total migration\" is the second migration strategy which offers in addition to the flexible querying, the possibility to store imprecise data. This strategy requires a modification of schemas, data and eventually programs.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124723836","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
Experimental Investigation of the Fault Tolerance of IDS Models IDS模型容错的实验研究
2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295667
M. Murakami, N. Honda
{"title":"Experimental Investigation of the Fault Tolerance of IDS Models","authors":"M. Murakami, N. Honda","doi":"10.1109/FUZZY.2007.4295667","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295667","url":null,"abstract":"The ink drop spread (IDS) method is a modeling technique that is proposed as a new paradigm of soft computing. The structure of IDS models is similar to that of artificial neural networks (ANNs): they comprise distributed processing units. The beneficial property of fault tolerance is obtained when such parallel processing networks are implemented with dedicated hardware. Among the ANNs, radial basis function networks (RBFNs) are known to possess superior fault tolerance. This study evaluates the fault tolerances of the IDS models and RBFNs using the approximation of continuous functions. The experimental results demonstrate that the IDS models are highly fault tolerant in comparison with the RBFNs.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125137820","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
Real-Time Fuzzy Predictive Control of a Column Flotation Process 某柱浮选过程的实时模糊预测控制
2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295610
S. Vieira, J. Sousa, F. Durão
{"title":"Real-Time Fuzzy Predictive Control of a Column Flotation Process","authors":"S. Vieira, J. Sousa, F. Durão","doi":"10.1109/FUZZY.2007.4295610","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295610","url":null,"abstract":"The process under study has four manipulating variables: feed flow rate, washing water, air and rejected flow rates. Column flotation process is an example of a complex, nonlinear and multivariable system. Fuzzy modeling is a well-known modeling technique, which has been applied to complex and nonlinear processes. Fuzzy multivariable modeling with fuzzy model predictive control is used in this paper to control a laboratory setup of a flotation column. Moreover, the control strategy is tested in real-time. Results show that the applied methods led to a good control performance of all the controlled variables.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116061402","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
BayesFuzzy: using a Bayesian Classifier to Induce a Fuzzy Rule Base 贝叶斯模糊:使用贝叶斯分类器生成模糊规则库
2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295637
Estevam Hruschka, H. Camargo, M. E. Cintra, M. C. Nicoletti
{"title":"BayesFuzzy: using a Bayesian Classifier to Induce a Fuzzy Rule Base","authors":"Estevam Hruschka, H. Camargo, M. E. Cintra, M. C. Nicoletti","doi":"10.1109/FUZZY.2007.4295637","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295637","url":null,"abstract":"Traditional algorithms for learning Bayesian classifiers (BCs) from data are known to induce accurate classification models. However, when using these algorithms, two main concerns should be considered: i) they require qualitative data and ii) generally the induced models are not easily comprehensible by human beings. This paper deals with the two above issues by proposing a hybrid method named BayesFuzzy that learns from quantitative data and induces a fuzzy rule based model that enhances comprehensibility. BayesFuzzy has been implemented as an automatic system that combines a fuzzy strategy, for transforming numerical data into qualitative information, with a Bayes-based approach for inducing rules. Promising empirical results of the use of the BayesFuzzy system in four knowledge domains are presented and discussed.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116527532","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: 4
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