2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)最新文献_第2页

Extending FML with evolving capabilities through a scripting language approach 通过脚本语言方法扩展具有不断发展的功能的FML

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2014-07-06 DOI: 10.1109/FUZZ-IEEE.2014.6891809

G. Acampora, M. Reformat, A. Vitiello

{"title":"Extending FML with evolving capabilities through a scripting language approach","authors":"G. Acampora, M. Reformat, A. Vitiello","doi":"10.1109/FUZZ-IEEE.2014.6891809","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2014.6891809","url":null,"abstract":"The introduction of Fuzzy Markup Language (FML) in 2004 has initiated an important trend in Computational Intelligence research: the application of new web technologies to create more flexible and hardware independent environment for deploying \"fuzzy ideas\". FML allows researchers and engineers to focus on problem solving activities bypassing additional difficulties related to programming or physical equipment constraints. From that moment on, many researches have been using FML and other XML-based languages for modeling and developing fuzzy systems. However, in spite of their hardware interoperability, XML languages are able to model a fuzzy system in static way and, consequently, they do not provide any support for modelling \"evolving\" and temporal-based fuzzy systems, as such Timed Automata based Fuzzy Controllers. To address this deficiency, this paper introduces an extension of FML called FMLScript. It is based on a scripting language concept and allows for modeling XML-based systems that can dynamically modify their configurations. As the consequence, a better expressive power can be achieved when compared with static modelling approaches. This is shown in a case study involving a smart grid control.","PeriodicalId":413342,"journal":{"name":"2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125340935","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 group decision-making based on variable weighted averaging operators 基于变加权平均算子的模糊群体决策

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2014-07-06 DOI: 10.1109/FUZZ-IEEE.2014.6891632

Deqing Li, Wenyi Zeng, Junhong Li

引用次数: 3

A new fuzzy approach for multi-source decision fusion 一种新的多源决策融合模糊方法

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2014-07-06 DOI: 10.1109/FUZZ-IEEE.2014.6891812

F. Fatemipour, M. Akbarzadeh-Totonchi, Rouhollah Ghasempour

{"title":"A new fuzzy approach for multi-source decision fusion","authors":"F. Fatemipour, M. Akbarzadeh-Totonchi, Rouhollah Ghasempour","doi":"10.1109/FUZZ-IEEE.2014.6891812","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2014.6891812","url":null,"abstract":"Nowadays, we are facing the rapidly growing amount of data being produced in many organizations, social networks and internet. These data are generated in disparate locations and their aggregation into one location is exceedingly time and space consuming. Traditional statistical methods are not sufficient for processing of this massive multi-source data. In this paper, we propose a new fuzzy-based decision fusion approach for classification problems of this kind. The necessity of fuzzy information arises in distributed classification because imprecision, uncertainty and ambiguity can be found at all information sources, from the data itself to the results of the classifiers. In the proposed approach, multiple classifiers are constructed based on different information sources which have different degrees of reliability. Then a fuzzy rule based system is designed for approximating distribution of reliabilities of sources over the input space. The decision fusion of multiple classifiers takes place using the estimated degrees of sources' reliabilities. Comparison results are made between both centralized classification and two other distributed classification methods. One is averaging and the other is discounting each classifier's decision based on its accuracy. Results show the high accuracy of the proposed method in making decisions in distributed environments, without the overhead of aggregating the entire data in one location.","PeriodicalId":413342,"journal":{"name":"2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115522457","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

Designing practical interval type-2 fuzzy logic systems made simple 使设计实用的区间2型模糊逻辑系统变得简单

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2014-07-06 DOI: 10.1109/FUZZ-IEEE.2014.6891534

Dongrui Wu, J. Mendel

{"title":"Designing practical interval type-2 fuzzy logic systems made simple","authors":"Dongrui Wu, J. Mendel","doi":"10.1109/FUZZ-IEEE.2014.6891534","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2014.6891534","url":null,"abstract":"Interval type-2 fuzzy logic systems (IT2 FLSs) have become increasingly popular in the last decade, and have demonstrated superior performance in a number of applications. However, the computations in an IT2 FLS are more complex than those in a type-1 FLS, and there are many choices to be made in designing an IT2 FLS, including the shape of membership functions (Gaussian or trapezoidal), number of membership functions, type of fuzzifier (singleton or non-singleton), kind of rules (Mamdani or Takagi-Sugeno-Kang), type of i-norm (minimum or product), method to compute the output (type-reduction or not), and methods for tuning the parameters (gradient-based methods or evolutionary computation algorithms; one-step or two-step). While these choices give an experienced IT2 FLS researcher extensive freedom to design the optimal IT2 FLS, they may look overwhelming and confusing to IT2 beginners. Such a beginner may make an inappropriate choice, obtain unexpected results, and lose interest, which will hinder the wider applications of IT2 FLSs. In this paper we try to help IT2 beginners navigate through the maze by recommending some representative choices for an IT2 FLS design. We also clarify two myths about IT2 FLSs. This paper will make IT2 FLSs more accessible to IT2 beginners.","PeriodicalId":413342,"journal":{"name":"2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116027332","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: 66

An under-sampling method based on fuzzy logic for large imbalanced dataset 基于模糊逻辑的大型不平衡数据欠采样方法

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2014-07-06 DOI: 10.1109/FUZZ-IEEE.2014.6891771

Ginny Y. Wong, Frank H. F. Leung, S. Ling

引用次数: 9

Fuzzy disturbance observer for a class of polynomial fuzzy control systems 一类多项式模糊控制系统的模糊扰动观测器

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2014-07-06 DOI: 10.1109/FUZZ-IEEE.2014.6891659

Hugang Han, Yuta Higaki, H. Lam

引用次数: 8

The experimenter environment of the NIP imperfection processor NIP缺陷处理器的实验环境

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2014-07-06 DOI: 10.1109/FUZZ-IEEE.2014.6891654

Raquel Martínez, J. M. Cadenas, M. C. Garrido

引用次数: 0

Genetic algorithm-aided dynamic fuzzy rule interpolation 遗传算法辅助的动态模糊规则插值

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2014-07-06 DOI: 10.1109/FUZZ-IEEE.2014.6891816

N. Naik, R. Diao, Q. Shen

引用次数: 32

Performance evaluation of interval type-2 and online rule weighing based Type-1 Fuzzy PID controllers on a pH process 基于区间2型和在线规则加权的1型模糊PID控制器对pH过程的性能评价

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2014-07-06 DOI: 10.1109/FUZZ-IEEE.2014.6891627

T. Kumbasar, Cihan Öztürk, E. Yesil, H. Hagras

{"title":"Performance evaluation of interval type-2 and online rule weighing based Type-1 Fuzzy PID controllers on a pH process","authors":"T. Kumbasar, Cihan Öztürk, E. Yesil, H. Hagras","doi":"10.1109/FUZZ-IEEE.2014.6891627","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2014.6891627","url":null,"abstract":"In this paper, we will explore whether the efficiency of the Interval Type-2 Fuzzy PID (IT2-FPID) lies in its ability to handle the high level of uncertainties rather than only having an extra degree of freedom provided by the Footprint of Uncertainty (FOU) on a highly nonlinear pH neutralization process. In order to illustrate the effect of the FOU on the control performance, the control performance of an IT2-FPID controller composed of 3×3 rules will be compared with a Type-1 Fuzzy PID (T1-FPID) controller of 5×5 rules. Moreover, in order to provide more extra degree of freedom to the T1-FPID structure, we will employ two self-tuning mechanisms where the weights of the fuzzy rules are adjusted in an online manner. Thus, we will present detailed comparative studies on how the extra degrees of freedom provided by the FOU or the employed tuning mechanisms affect the control and robustness performance. The presented analysis confirm that by tuning the FOU the performance of the IT2-FPID is better in wide range of operating points in comparison with its type-1 and self-tuning type-1 fuzzy counterparts which is not merely for the IT2-FPID use of extra parameters, but rather its different way of dealing with the disturbance, nonlinearities uncertainties and noise.","PeriodicalId":413342,"journal":{"name":"2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114578213","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

A preprocessed induced partition matrix based collaborative fuzzy clustering for data analysis 一种基于预处理诱导划分矩阵的协同模糊聚类方法

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2014-07-06 DOI: 10.1109/FUZZ-IEEE.2014.6891876

M. Prasad, Linda Siana, Dong-Lin Li, Chin-Teng Lin, Yu-Ting Liu, A. Saxena

{"title":"A preprocessed induced partition matrix based collaborative fuzzy clustering for data analysis","authors":"M. Prasad, Linda Siana, Dong-Lin Li, Chin-Teng Lin, Yu-Ting Liu, A. Saxena","doi":"10.1109/FUZZ-IEEE.2014.6891876","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2014.6891876","url":null,"abstract":"Preprocessing is generally used for data analysis in the real world datasets that are noisy, incomplete and inconsistent. In this paper, preprocessing is used to refine the inconsistency of the prototype and partition matrices before getting involved in the collaboration process. To date, almost all organizations are trying to establish some collaboration with others in order to enhance the performance of their services. Due to privacy and security issues they cannot share their information and data with each other. Collaborative clustering helps this kind of collaborative process while maintaining the privacy and security of data and can still yield a satisfactory result. Preprocessing helps the collaborative process by using an induced partition matrix generated based on cluster prototypes. The induced partition matrix is calculated from local data by using the cluster prototypes obtained from other data sites. Each member of the collaborating team collects the data and generates information locally by using the fuzzy c-means (FCM) and shares the cluster prototypes to other members. The other members preprocess the centroids before collaboration and use this information to share globally through collaborative fuzzy clustering (CFC) with other data. This process helps system to learn and gather information from other data sets. It is found that preprocessing helps system to provide reliable and satisfactory result, which can be easily visualized through our simulation results in this paper.","PeriodicalId":413342,"journal":{"name":"2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116836340","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: 15