2007 IEEE International Fuzzy Systems Conference最新文献_第3页

Fuzzy Logic Obstacle Identity Declaration and Fusion in the Autotaxi System 自动出租车系统中模糊逻辑障碍的识别与融合

2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295545

P. J. Escamilla-Ambrosio, N. Lieven

{"title":"Fuzzy Logic Obstacle Identity Declaration and Fusion in the Autotaxi System","authors":"P. J. Escamilla-Ambrosio, N. Lieven","doi":"10.1109/FUZZY.2007.4295545","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295545","url":null,"abstract":"The Autotaxi system is a safety critical sensor system developed to perform the sensing required for an autonomous vehicle to drive safely along a dedicated paved guideway network. The host vehicle is equipped with a set of sensors used to detect and track any object of interest in the field of view. In this work a multiple-sensor obstacle identification and fusion approach for the Autotaxi system is proposed. Based on the knowledge about the vehicles, the obstacles to be detected, and the guideway network system, two obstacle classifier systems are designed using the principles of fuzzy logic. In Classifier 1 the classification process is carried out based on the obstacle's width and kind of road in which the host vehicle is navigating. In Classifier 2 the classification process is carried out based on the obstacle's width and height together with the kind of road in which the host vehicle is navigating. Furthermore, as different declarations of identity can be performed by using information from different sensors, a method to fuse these identity declarations is proposed. The viability of the proposed approach is demonstrated through a simulated example. Promising results are reported.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"31 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":"114749212","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

Semi-Supervised Clustering and Feature Discrimination with Instance-Level Constraints 具有实例级约束的半监督聚类和特征识别

2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295625

H. Frigui, R. Mahdi

引用次数: 4

Adaptive Fuzzy Sliding-Mode Control for Variable Displacement Hydraulic Servo System 变排量液压伺服系统的自适应模糊滑模控制

2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295434

M. Chiang, Lian-Wang Lee, Hsien-Hsush Liu

引用次数: 7

New Type-2 Rule Ranking Indices for Designing Parsimonious Interval Type-2 Fuzzy Logic Systems 设计简约区间2型模糊逻辑系统的新2型规则排序指标

2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295477

Shang-Ming Zhou, R. John, F. Chiclana, J. Garibaldi

引用次数: 6

Management of Ignorance by Interval Probability 区间概率的无知管理

2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295475

T. Entani, Hideo Tanaka

引用次数: 6

A Multi-Objective Evolutionary Algorithm for Rule Selection and Tuning on Fuzzy Rule-Based Systems 基于模糊规则系统的多目标规则选择与优化进化算法

2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295566

R. Alcalá, J. Alcalá-Fdez, M. J. Gacto, F. Herrera

引用次数: 17

Introducing Class-Based Classification Priority in Fuzzy Rule-Based Classification Systems 模糊规则分类系统中基于类的分类优先级

2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295632

T. Nakashima, Y. Yokota, G. Schaefer, H. Ishibuchi

引用次数: 3

Learning Fuzzy Linguistic Models from Low Quality Data by Genetic Algorithms 用遗传算法从低质量数据中学习模糊语言模型

2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295659

L. Sánchez, J. Otero

引用次数: 14

Evolving Single- And Multi-Model Fuzzy Classifiers with FLEXFIS-Class 基于flexfi - class的单模型和多模型模糊分类器演化

2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295393

E. Lughofer, P. Angelov, Xiaowei Zhou

{"title":"Evolving Single- And Multi-Model Fuzzy Classifiers with FLEXFIS-Class","authors":"E. Lughofer, P. Angelov, Xiaowei Zhou","doi":"10.1109/FUZZY.2007.4295393","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295393","url":null,"abstract":"In this paper a new method for training single-model and multi-model fuzzy classifiers incrementally and adaptively is proposed, which is called FLEXFIS-Class. The evolving scheme for the single-model case exploits a conventional zero-order fuzzy classification model architecture with Gaussian fuzzy sets in the rules antecedents, crisp class labels in the rule consequents and rule weights standing for confidence values in the class labels. In the multi-model case FLEXFIS-Class exploits the idea of regression by an indicator matrix to evolve a Takagi-Sugeno fuzzy model for each separate class and combines the single models' predictions to a final classification statement. The paper includes a technique for increasing the prediction quality, whenever a drift in a data stream occurs. An empirical analysis will be given based on an online, adaptive image classification framework, where images showing production items should be classified into good or bad ones. This analysis will include the comparison of evolving single-and multi-model fuzzy classifiers with conventional batch modelling approaches with respect to achieved prediction accuracy on new online data. It will also be shown that multi-model architecture can outperform conventional single-model architecture ('classical' fuzzy classification models) for all data sets with respect to prediction accuracy.","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":"130791453","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: 46

On Storing Ontologies including Fuzzy Datatypes in Relational Databases 关系型数据库中包含模糊数据类型的本体存储

2007 IEEE International Fuzzy Systems Conference Pub Date : 2007-07-23 DOI: 10.1109/FUZZY.2007.4295624

Carlos D. Barranco, Jesús R. Campaña, J. M. Medina, O. Pons

引用次数: 20