2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)最新文献_第7页

A consensus process for hesitant fuzzy linguistic preference relations 犹豫模糊语言偏好关系的共识过程

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337827

Zhibin Wu

引用次数: 9

Approaches to interval type-2 fuzzy multiple attribute group decision making based on grey incidence analysis and FTP utility function 基于灰色关联分析和FTP效用函数的区间型2模糊多属性群决策方法

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337823

Jindong Qin, Junfeng Chu, Xinwang Liu, W. Pedrycz

引用次数: 2

A fuzzy ANP with interval type-2 fuzzy sets approach to evaluate enterprise technological innovation ability 基于区间2型模糊集的模糊ANP评价企业技术创新能力

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337987

Tong Wu, Xinwang Liu, Shuli Liu

引用次数: 9

Wavelet based fuzzy clustering technique for the extraction of road objects 基于小波模糊聚类技术的道路目标提取

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337887

Tejy Kinattukara, B. Verma

引用次数: 2

Circumcenter of centroid in ranking fuzzy number: A case of generalized trapezoidal fuzzy numbers 排序模糊数的质心圆心:广义梯形模糊数的一个例子

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337941

L. Abdullah, Fateen Najwa Azman

引用次数: 1

A taxonomy for multiple attribute group decision making literature 多属性组决策文献的分类

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7338114

Bilal Ervural, Özgür Kabak

引用次数: 8

Adaptive interval type-2 fuzzy sliding mode controller design for flexible air-breathing hypersonic vehicles 柔性吸气式高超声速飞行器自适应区间2型模糊滑模控制器设计

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337828

Junlong Gao, Ruyi Yuan, J. Yi, Chengdong Li

{"title":"Adaptive interval type-2 fuzzy sliding mode controller design for flexible air-breathing hypersonic vehicles","authors":"Junlong Gao, Ruyi Yuan, J. Yi, Chengdong Li","doi":"10.1109/FUZZ-IEEE.2015.7337828","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337828","url":null,"abstract":"In this paper an adaptive interval type-2 fuzzy sliding mode controller, which is applied to flexible air-breathing hypersonic vehicle (FAHV) longitudinal model, is designed based on interval type-2 fuzzy logic systems (IT2-FLS) and sliding mode control (SMC) theory. In order to get FAHV longitudinal model stably controlled, we decouple the model into velocity and altitude channels through output feedback linearization. Moreover, due to the severe uncertainties which mainly come from unpredictable varying aerodynamic interferences and mutual couplings in airframe flexible modes and those difficulties of computing nonlinear functions with high-order derivatives under practical conditions, we design a sliding mode controller to achieve system convergence and adopt IT2-FLS to estimate the nonlinear functions with bounded parameter uncertainties online for counteracting the tracking errors and suppressing flexible vibrations. The adaptive law of interval type-2 fuzzy sliding mode controller is derived through Lyapunov synthesis approach. Furthermore, we adopt tracking differentiator (TD) and nonlinear state observer (NSO) algorithms to generate the real-time derivatives and high-order approximate commands in velocity and altitude channels, respectively. Several comparisons have been done in this paper and the simulation results validate the robustness and effectiveness of the proposed controller.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"15 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":"133398774","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

A Multilayer Fuzzy Cognitive Maps approach to the cloud adoption decision support problem 云采用决策支持问题的多层模糊认知图方法

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7338056

Andreas Christoforou, A. Andreou

{"title":"A Multilayer Fuzzy Cognitive Maps approach to the cloud adoption decision support problem","authors":"Andreas Christoforou, A. Andreou","doi":"10.1109/FUZZ-IEEE.2015.7338056","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7338056","url":null,"abstract":"As modern computing relies more and more on distributed solutions of services and resources over the cloud, the need of potential users to assess whether the transition from traditional software systems to the cloud would be to their benefit becomes even greater. Cloud vendors also seek ways to study beforehand the behavior of potential users with respect to their decision to adopt the cloud environment so as to take actions towards enhancing the positive side. Therefore, the study of the parameters forming the environment behind the cloud adoption decision is of paramount importance to both users and vendors. In this context the present paper proposes a multi-layer FCM approach which models a number of factors which play a decisive role to the cloud adoption issue and offers the means to study their influence. The factors are organized in different layers which focus on specific aspects of the cloud environment, something which, on one hand, enables tracking the causes for the decision outcome, and on the other offers the ability to study the dependencies between the leading determinants of the decision. The construction and analysis of the model is based on factors reported in the relevant literature and the utilization of experts' opinion. The efficacy and applicability of the proposed approach are demonstrated through four real-world experimental cases.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"89 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":"116745843","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 linguistic aggregation to synthesize the Hourglass of Emotions 模糊语言聚合合成情感沙漏

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7338020

Carmine Brenga, Antonio Celotto, V. Loia, S. Senatore

引用次数: 3

A big-data processing framework for uncertainties in transportation data 交通数据不确定性的大数据处理框架

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337843

Jie Yang, Jun Ma

{"title":"A big-data processing framework for uncertainties in transportation data","authors":"Jie Yang, Jun Ma","doi":"10.1109/FUZZ-IEEE.2015.7337843","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337843","url":null,"abstract":"Transportation infrastructure takes a primary role in urban development planning. To better facilitate or understand the infrastructure status and demands, a huge amount of transportation data such as traffic flow counts has been collected from numerous transportation monitoring systems. Making full use of harvested data samples to discover important patterns has become an increasingly appealing research topic, in which a sophisticated and uncertainty-processing framework is required. In this paper, a big-data processing framework is introduced to analyse the transportation data, particularly taking the classification problem of the parking occupation status as an illustrative example. Three modules are implemented to crawl the raw records, generate high-level features, and apply the machine learning algorithm for classification. In addition, the fuzzification algorithm is also introduced to quantify the key attributes of the data, which helps in removing the data redundancy and inconsistency. The proposed framework then is evaluated using a real-world dataset collected from twelve car parks in a university. Simulation results show that the proposed framework performs well with a convincing classification accuracy.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"117 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":"133189115","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: 8