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2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)最新文献

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Generalized Predictive Control using Interval Type-2 Fuzzy models 区间2型模糊模型的广义预测控制
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337967
Rómulo Antão, A. Mota, Rui Escadas Martins
{"title":"Generalized Predictive Control using Interval Type-2 Fuzzy models","authors":"Rómulo Antão, A. Mota, Rui Escadas Martins","doi":"10.1109/FUZZ-IEEE.2015.7337967","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337967","url":null,"abstract":"The development of Interval Type-2 Fuzzy Logic Systems has brought great improvements in the non-linear system modeling domain. However, in what concerns to the development of control systems, the approaches found in literature of Type-2 Fuzzy Sets do not seem to be taking fully advantage of the advances achieved by adaptive self-tuning algorithms, already well established in both academic and industrial communities. This work presents how a controller based on Generalized Predictive Control (GPC) theory can be developed based on an Interval Type-2 Takagi-Sugeno Fuzzy Model, providing details regarding the online model training mechanisms and controller parameter's synthesis. This approach is then compared with two additional GPC implementations based on an Auto-Regressive model with eXogenous inputs (ARX) and Type-1 Takagi-Sugeno Fuzzy models. A Multiple-Input-Single-Output (MISO) Coupled Tank System will serve as benchmark system to evaluate the reference tracking capability and robustness of the controllers when subjected to different operation points and several unmodeled disturbances.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"27 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":"124482568","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
Linguistic fuzzy logic in R 语言模糊逻辑
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337826
M. Burda
{"title":"Linguistic fuzzy logic in R","authors":"M. Burda","doi":"10.1109/FUZZ-IEEE.2015.7337826","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337826","url":null,"abstract":"The aim of this paper is to present a new package for the R statistical environment that enables the use of linguistic fuzzy logic in data processing applications. The lfl package provides tools for transformation of data into fuzzy sets representing linguistic expressions, for mining of linguistic fuzzy association rules, and for performing an inference on fuzzy rule bases using the Perception-based Logical Deduction (PbLD). The package also contains a Fuzzy Rule-based Ensemble, a tool for time series forecasting based on an ensemble of forecasts from several individual methods that is driven by a linguistic rule base created automatically from a large set of training time series.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"41 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":"126120694","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
Handling uncertainty in cloud resource management using fuzzy Bayesian networks 利用模糊贝叶斯网络处理云资源管理中的不确定性
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337979
F. Ramezani, M. Naderpour, Jie Lu
{"title":"Handling uncertainty in cloud resource management using fuzzy Bayesian networks","authors":"F. Ramezani, M. Naderpour, Jie Lu","doi":"10.1109/FUZZ-IEEE.2015.7337979","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337979","url":null,"abstract":"The success of cloud services depends critically on the effective management of virtualized resources. This paper aims to design and implement a decision support method to handle uncertainties in resource management from the cloud provider perspective that enables underlying complexity, automates resource provisioning and controls client-perceived quality of service. The paper includes a probabilistic decision making module that relies upon a fuzzy Bayesian network to determine the current situation status of a cloud infrastructure, including physical and virtual machines, and predicts the near future state, that will help the hypervisor to migrate or expand the VMs to reduce execution time and meet quality of service requirements. First, the framework of resource management is presented. Second, the decision making module is developed. Lastly, a series of experiments to investigate the performance of the proposed module is implemented. Experiments reveal the efficiency of the module prototype.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"35 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":"128287707","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
Hybrid model for medical diagnosis using Neutrosophic Cognitive Maps with Genetic Algorithms 基于遗传算法的嗜中性认知图谱医学诊断混合模型
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7338015
Gaurav, Megha Kumar, Kanika Bhutani, Swati Aggarwal
{"title":"Hybrid model for medical diagnosis using Neutrosophic Cognitive Maps with Genetic Algorithms","authors":"Gaurav, Megha Kumar, Kanika Bhutani, Swati Aggarwal","doi":"10.1109/FUZZ-IEEE.2015.7338015","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7338015","url":null,"abstract":"This paper presents a combination of two soft computing techniques: Neutrosophic cognitive maps and Genetic algorithm for modeling of medical disease diagnosis. Earlier, medical decision support system was proposed by many researchers where cognitive maps along with genetic algorithm were experimented. The hybrid model of fuzzy cognitive maps with genetic algorithm was implemented to handle situations where decisions are not clearly distinct. In real world situation data is not always consistent so, authors proposed a new hybrid model of Neutrosophic Cognitive Maps with Genetic Algorithms to handle indeterminacy. The proposed model will provide distinct diagnosis of disease in medical decision support system.","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":"128425458","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: 41
Two decision making models based on newly defined additively consistent intuitionistic preference relation 基于新定义的加性一致直觉偏好关系的两个决策模型
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337953
Junfeng Chu, Xinwang Liu, Zaiwu Gong
{"title":"Two decision making models based on newly defined additively consistent intuitionistic preference relation","authors":"Junfeng Chu, Xinwang Liu, Zaiwu Gong","doi":"10.1109/FUZZ-IEEE.2015.7337953","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337953","url":null,"abstract":"In this paper, we introduce the concept called fuzzy non-preferred relation where the elements are interpreted as the non-preferred intensity of one alternative over another one. We divide an intuitionistic preference relation into the fuzzy preference relation part and the fuzzy non-preferred relation part . Based on this division, we propose a new definition of additive consistency of intuitionistic preference relation based on the traditional one. Then, we construct an optimization model for deriving intuitionistic fuzzy weights which can be ranked easily in individual decision making environment. And we develop an optimization model for deriving the collective intuitionistic fuzzy weights to address group decision making problems. Finally, two numerical examples are provided to illustrate the developed approaches.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"27 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":"130637291","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
Timed Fuzzy Cognitive Maps 定时模糊认知地图
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7338074
E. Bourgani, C. Stylios, G. Manis, V. Georgopoulos
{"title":"Timed Fuzzy Cognitive Maps","authors":"E. Bourgani, C. Stylios, G. Manis, V. Georgopoulos","doi":"10.1109/FUZZ-IEEE.2015.7338074","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7338074","url":null,"abstract":"Taking time into consideration in the FCM model and the subsequent numerical calculation is a hot FCM issue. This work investigates an approach for introducing the time in Fuzzy Cognitive Maps (FCM) framework and it proposes the Timed-FCM (T-FCM) that incorporates time steps in their structure and constitutes an extension of FCMs. The proposed T-FCMs take into consideration the time evolution and the fact that the parameters of any system change with the time and it accepts intermediate states and results of the modeled system. In any problem, its factors change differently over time and so their influence as is presented on the interconnections between the concepts are time dependent.","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":"129201207","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
An automatic corpus based method for a building Multiple Fuzzy Word Dataset 基于语料库的多模糊词数据集自动构建方法
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337877
David Chandran, Keeley A. Crockett, D. Mclean, Alan Crispin
{"title":"An automatic corpus based method for a building Multiple Fuzzy Word Dataset","authors":"David Chandran, Keeley A. Crockett, D. Mclean, Alan Crispin","doi":"10.1109/FUZZ-IEEE.2015.7337877","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337877","url":null,"abstract":"Fuzzy sentence semantic similarity measures are designed to be applied to real world problems where a computer system is required to assess the similarity between human natural language and words or prototype sentences stored within a knowledge base. Such measures are often developed for a specific corpus/domain where a limited set of words and sentences are evaluated. As new “fuzzy” measures are developed the research challenge is on how to evaluate them. Traditional approaches have involved rigorous and complex human involvement in compiling benchmark datasets and obtaining human similarity measures. Existing datasets often contain limited fuzzy words and do allow the fuzzy measures to be exhaustively tested. This paper presents an automatic method for the generation of a Multiple Fuzzy Word Dataset (MFWD) from a corpus. A Fuzzy Sentence Pairing Algorithm is used to extract and augment high, medium and low similarity sentence pairs with multiple fuzzy words. Human ratings are collected through crowdsourcing and the MFWD is evaluated using both fuzzy and traditional sentence similarity measures. The results indicated that fuzzy measures returned a higher correlation with human ratings compared with traditional measures.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"411 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":"123386762","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
Fuzzy set points & predicitve functional control for implicit regulator calculation of MIMO-systems mimo系统隐式调节器计算的模糊设定点与预测函数控制
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7338042
T. Aissa, Christian Arnold, S. Lambeck
{"title":"Fuzzy set points & predicitve functional control for implicit regulator calculation of MIMO-systems","authors":"T. Aissa, Christian Arnold, S. Lambeck","doi":"10.1109/FUZZ-IEEE.2015.7338042","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7338042","url":null,"abstract":"Controlling states or outputs of technical processes to exact set points is not necessary in some technical applications. It may be sufficient to keep the state variables in defined regions. This fact can be utilized to formulate set point intervals. Hence the chance to reduce variations in manipulated signals and to increase the durability of actuators is offered. This paper presents an approach of using set point intervals for the reduction of variations of the manipulated signals. We introduce a combination of fuzzy decision making and predictive functional control for time-discrete state-space models. The performance of the proposed controller approach will be demonstrated and compared to other controllers at a commonly known three-tank system.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"9 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":"121347586","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
A Choquet integral-based model for sustainable performance of suppliers 基于Choquet积分的供应商可持续绩效模型
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337943
Ege Guray, A. Yayla, K. Yıldız
{"title":"A Choquet integral-based model for sustainable performance of suppliers","authors":"Ege Guray, A. Yayla, K. Yıldız","doi":"10.1109/FUZZ-IEEE.2015.7337943","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337943","url":null,"abstract":"The supplier selection process of a company depends on numerous criteria which can have differing importance factors. Solving this kind of multiple criteria decision making (MCDM) problem is essential for the long-term strategy of a company. While this problem is examined numerous times throughout the years, the consideration of involving economic, social and environmental factors, i.e. sustainability is becoming a growing necessity for this effort, especially in the recent years. We propose a Choquet integral based fuzzy model to use the data we obtain by converting the linguistic terms of perceived subcontractor performance values to trapezoidal fuzzy numbers. After the performance criteria values are normalized by the fuzzy integral, they are defuzzified into single data points which helps an evaluator to compare. A case study involving a curtain wall company in Turkey (and its 4 subcontractors) is used to demonstrate the effectiveness of the method. The defuzzified results showed which subcontractor is the optimal choice among 4 subcontractors and 17 criteria.","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":"122218140","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
Insights and characterization of l1-norm based sparsity learning of a lexicographically encoded capacity vector for the Choquet integral Choquet积分的字典编码容量向量的基于11范数的稀疏性学习的见解和表征
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337819
Titilope A. Adeyeba, Derek T. Anderson, T. Havens
{"title":"Insights and characterization of l1-norm based sparsity learning of a lexicographically encoded capacity vector for the Choquet integral","authors":"Titilope A. Adeyeba, Derek T. Anderson, T. Havens","doi":"10.1109/FUZZ-IEEE.2015.7337819","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337819","url":null,"abstract":"The aim of this paper is the simultaneous minimization of model error and model complexity for the Choquet integral. The Choquet integral is a generator function, that is, a parametric function that yields a wealth of aggregation operators based on the specifics of the underlying fuzzy measure (aka normal and monotonic capacity). It is often the case that we desire to learn an aggregation operator from data and the goal is to have the smallest possible sum of squared error (SSE) between the trained model and a set of labels or function values. However, we also desire to learn the “simplest” solution possible, viz., the model with the fewest number of inputs. Previous works focused on the use of l1-norm regularization of a lexicographically encoded capacity vector relative to the Choquet integral, describing how to carry out the procedure and demonstrating encouraging results. However, no characterization or insights into the capacity and integral were provided. Herein, we investigate the impact of l1-norm regularization of a lexicographically encoded capacity vector in terms of what capacities and aggregation operators it strives to induce in different scenarios. Ultimately, this provides insight into what the regularization is really doing and when to apply such a method. Synthetic experiments are performed to illustrate the remarks, propositions, and concepts put forth.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"26 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":"126104612","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
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