2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)最新文献_第10页

2-Maximum spanning fuzzy values-based splitting: an iterative clustering algorithm 基于最大生成模糊值的分裂:一种迭代聚类算法

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337803

Minyar Sassi Hidri, Mohamed Amine Baatout

引用次数: 1

How to estimate expected shortfall when probabilities are known with interval or fuzzy uncertainty 当概率已知且具有区间不确定性或模糊不确定性时，如何估计预期缺口

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337895

C. Servin, H. Nguyen, V. Kreinovich

引用次数: 1

On group-like FLe-algebras 关于类群fl -代数

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337814

S. Jenei

引用次数: 0

Direct adaptive fuzzy sliding mode PI tracking control of a three-dimensional overhead crane 三维桥式起重机的直接自适应模糊滑模PI跟踪控制

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337856

Tsung-Chih Lin, Jian-Lin Yeh, Tseng-Chuan Hung, Chia-Hao Kuo

引用次数: 5

Evolving Complex-Valued Interval Type-2 Fuzzy Inference System 演化的复值区间2型模糊推理系统

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7338088

K. Subramanian, S. Sundaram

{"title":"Evolving Complex-Valued Interval Type-2 Fuzzy Inference System","authors":"K. Subramanian, S. Sundaram","doi":"10.1109/FUZZ-IEEE.2015.7338088","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7338088","url":null,"abstract":"Interval Type-2 fuzzy systems have been shown to be extremely capable of handling vagueness as well as uncertainty in data, while complex-valued fuzzy sets have been demonstrated to be capable of solving classification problems efficiently. This paper combines their collective advantage to propose a complex-valued Interval Type-2 Fuzzy Inference System (referred to as CIT2FIS). To derive the fuzzy rules, a Recursive Least Squares based algorithm is proposed. The proposed algorithm evolves (add/ prune) and adapts the rules in an evolving online fashion. During sequential learning, the network monitors the error and knowledge contained in the current sample and either rules are evolved (added, pruned) to capture the knowledge in the sample, or the rule parameter updated. Upon rule addition, the centers are determined based on the current input and the output weights are analytically determined such that a least squares fit is obtained. This ensures that the rule retain its interpretability and accuracy. Parameter update is based on recursive least squares based approach. In order to maintain the parsimony of the network, a data-driven rule pruning scheme is employed. To further enhance the generalization ability of the network, wellknown meta-cognitive learning mechanism is employed in this work. The performance of the proposed CIT2FIS is evaluated on a set of real-valued classification problems. The performance comparison with other state-of-the-art complex-valued as well as fuzzy classifiers clearly highlights the advantage of the proposed work.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115945621","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

Combining quantifications for flexible query result ranking 结合量化，实现灵活的查询结果排序

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7338083

Christophe Billiet, G. Tré

引用次数: 1

Fuzzy objects and closure operators 模糊对象和闭包操作符

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337879

J. Močkoř

引用次数: 0

Fast and economic integration of new classes on the fly in evolving fuzzy classifiers using class decomposition 基于类分解的进化模糊分类器中新类的快速经济集成

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337846

E. Lughofer, Eva Weigl, Wolfgang Heidl, C. Eitzinger, Thomas Radauer

{"title":"Fast and economic integration of new classes on the fly in evolving fuzzy classifiers using class decomposition","authors":"E. Lughofer, Eva Weigl, Wolfgang Heidl, C. Eitzinger, Thomas Radauer","doi":"10.1109/FUZZ-IEEE.2015.7337846","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337846","url":null,"abstract":"In this paper, we propose a fast and economic strategy for the integration of new classes on the fly into evolving fuzzy classifiers (EFC) during data stream mining processes. Fastness addresses the assurance that a newly arising class in the stream can be integrated in a way such that the classifier is able to correctly return the new class after receiving only a few training samples of it. Economic means that the classifier update cycles are decreased to a minimum amount of time, as these require operator's feedback for obtaining the ground truth labels, which are usually costly to obtain. The former is achieved by a class-decomposition approach, which splits up multi-class classification problems into several less imbalanced and less complex binary sub-problems. The latter is achieved by a single-pass active learning selection scheme which selects the most informative samples based on sample-wise criteria. The approach is compared with conventional single model architecture for EFC (EFC-SM) based on two data streams from a real-world application in the field of surface inspection. The comparison shows that the class decomposition approach can significantly reduce the delay of class integration, and this with a lower # of samples used for model updates than EFC-SM.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116869044","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

On the consideration of a bring-to-mind model for computing the Information Content of concepts defined into ontologies 考虑一种用于计算定义为本体的概念的信息内容的联想模型

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337964

S. Harispe, A. Imoussaten, F. Trousset, J. Montmain

引用次数: 14

Shape-dependent maximal controllable sets for constrained discrete-time TS systems 约束离散时间TS系统的形状相关最大可控集

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7338071

C. Ariño, A. Sala

引用次数: 3