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2006 International Symposium on Evolving Fuzzy Systems最新文献

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An Adaptive Genetic-Based Architecture for the On-line Co-ordination of Fuzzy Embedded Agents with Multiple Objectives and Constraints 多目标约束模糊嵌入式智能体在线协调的自适应遗传结构
2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251145
E. Tawil, H. Hagras
{"title":"An Adaptive Genetic-Based Architecture for the On-line Co-ordination of Fuzzy Embedded Agents with Multiple Objectives and Constraints","authors":"E. Tawil, H. Hagras","doi":"10.1109/ISEFS.2006.251145","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251145","url":null,"abstract":"This paper presents a novel embedded agent architecture that aims to co-ordinate a system of interacting embedded agents in real-world intelligent environments using a unique on-line multi-objective and multi-constraint genetic algorithm. The embedded agents can be complex ones such as mobile robots that would operate hierarchical fuzzy logic controllers or simple ones such as desk lamps that would bear threshold functions instead. The architecture would enable the agents to learn the users' desires and act based on them in real-time without having to repeatedly configure the system. The system can handle unreliable sensors and actuators as well as compensating for agents that break down and adapting on-line to sudden changes. The architecture allows for the organisation of agents to be dynamic since it accommodates for agents migrating in and out of the system. Multifarious experiments were performed on implementations of the aforementioned architecture where the system was tested in different scenarios of varying circumstances","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133562456","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
Genetic Iterative Feedback Tuning (GIFT) Method for Fuzzy Control System Development 遗传迭代反馈整定方法在模糊控制系统开发中的应用
2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251133
R. Precup, S. Preitl
{"title":"Genetic Iterative Feedback Tuning (GIFT) Method for Fuzzy Control System Development","authors":"R. Precup, S. Preitl","doi":"10.1109/ISEFS.2006.251133","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251133","url":null,"abstract":"This paper proposes an original iterative feedback tuning (IFT) method employing genetic algorithms to develop a class of fuzzy control systems. The approach is based on using the linear case results from the original IFT method and on replacing the parameter update law by genetic algorithms. Then, these results are transferred to the fuzzy case in terms of the modal equivalence principle resulting in an attractive development method referred to as genetic iterative feedback tuning (GIFT). The GIFT method is applied to the development of fuzzy control systems with PI-fuzzy controllers dedicated to a class of integral type servo systems, where the linear case is focused on the IFT method in connection with the extended symmetrical optimum method to obtain the initial values of the linear PI controller parameters. Real-time experimental results corresponding to a fuzzy controlled nonlinear servo system are presented to validate the development method","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129222766","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
Fuzzy Linguistic Query-based User Profile Learning by Multiobjective Genetic Algorithms 基于模糊语言查询的多目标遗传算法的用户轮廓学习
2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251152
O. Cordón, E. Herrera-Viedma, M. Luque
{"title":"Fuzzy Linguistic Query-based User Profile Learning by Multiobjective Genetic Algorithms","authors":"O. Cordón, E. Herrera-Viedma, M. Luque","doi":"10.1109/ISEFS.2006.251152","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251152","url":null,"abstract":"In this paper, a multiobjective genetic algorithm is proposed to automatically learn persistent fuzzy linguistic queries for text retrieval applications. These queries are able to represent user's long-term standing information needs in a more interpretable way than the classical \"bag of words\" user profile structure. Thanks to its multiobjective nature, the introduced genetic fuzzy system is able to build different queries for the same information need in a single run, with a different trade-off between precision and recall. The experiments performed on the classical CACM collection show that although the different queries obtained from our genetic fuzzy system are less accurate in the retrieval task than those derived by one state-of-the-art bag of words method, they compose more flexible, comprehensible and expressive user profiles","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123794321","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
On the Use of Data Driven and Fuzzy Techniques to Calculate the Wind Speed in Urban Canyons 数据驱动和模糊技术在城市峡谷风速计算中的应用
2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251181
M. Santamouris, C. Georgakis, A. Niachou
{"title":"On the Use of Data Driven and Fuzzy Techniques to Calculate the Wind Speed in Urban Canyons","authors":"M. Santamouris, C. Georgakis, A. Niachou","doi":"10.1109/ISEFS.2006.251181","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251181","url":null,"abstract":"The present per presents the results of an extensive study aiming to develop and validate alternative data driven techniques able to estimate the wind speed in urban canyons. The use of deterministic techniques to calculate the wind speed in canyons present a low accuracy because of the high uncertainty of the input data and the incomplete description of the physical phenomena. (C. Georgakis et al., 2004) Extended experimental data collected from seven urban canyons have been used to create a data base of the main parameters that define the phenomenon. Using fuzzy clustering techniques, clusters of input-output data have been developed using as criteria the inertia and gravitational forces. For each cluster using statistical analysis, the more probable wind speed inside the canyon and the corresponding input values have been estimated. Thus, a reduced data space has been created. This reduced data space has been used to develop four data driven prediction models. The models are : a 3D graphical interpolation method, a tree based model as well as a linear regression model. Using the results of the graphical interpolation model, a fuzzy estimation model has been developed as well. All methods have been compared against the experimental data","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129060275","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
Gate-position and Turbine-generator Unit Speed Signal Approximation with Fuzzy Clustering for TS Fuzzy Model TS模糊模型的门位置和汽轮发电机组转速信号模糊聚类逼近
2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251130
Nand Kishor, S. P. Singh, A. S. Raghuvanshi, P. Sharma
{"title":"Gate-position and Turbine-generator Unit Speed Signal Approximation with Fuzzy Clustering for TS Fuzzy Model","authors":"Nand Kishor, S. P. Singh, A. S. Raghuvanshi, P. Sharma","doi":"10.1109/ISEFS.2006.251130","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251130","url":null,"abstract":"Takagi-Sugeno (TS) type of fuzzy models for data-driven set has attracted a great deal of attention of the fuzzy modeling community due to its satisfactory performance in various applications. In this paper, an application of TS model is described to obtain approximated input-output response of turbine-generator unit in a hydro power plant operating as an isolated system. The rule-base is generated independently each for input and output response using fuzzy c-mean (FCM) and Gustafson-Kessel (GK) algorithms with antecedents determined using product space. The model simulations are demonstrated for 50% decrease and 10% increase in load disturbance from rated conditions","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126704914","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
Participatory Evolving Fuzzy Modeling 参与式演化模糊模型
2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251135
E. Lima, F. Gomide, R. Ballini
{"title":"Participatory Evolving Fuzzy Modeling","authors":"E. Lima, F. Gomide, R. Ballini","doi":"10.1109/ISEFS.2006.251135","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251135","url":null,"abstract":"This paper introduces an approach to develop evolving fuzzy rule-based models based on the idea of participatory learning. Participatory learning is a means to learn and revise beliefs based on what is already known or believed. Participatory learning naturally induces unsupervised dynamic fuzzy clustering algorithms and provides an effective alternative construct evolving functional fuzzy models and adaptive fuzzy systems. Evolving participatory learning is used to forecast average weekly inflows for hydroelectric generation purposes and compared with eTS, an evolving modeling technique that uses the notion of potential to dynamically cluster data","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129201686","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: 38
A Self-Organizing Fuzzy Polynomial Neural Network - Multistage Classifier 自组织模糊多项式神经网络-多阶段分类器
2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251177
N. Mitrakis, J. Theocharis
{"title":"A Self-Organizing Fuzzy Polynomial Neural Network - Multistage Classifier","authors":"N. Mitrakis, J. Theocharis","doi":"10.1109/ISEFS.2006.251177","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251177","url":null,"abstract":"A fuzzy polynomial neural network multistage classifier (FPNN-MC) is suggested in this paper, suitable for handling complex classification problems with large feature spaces. The multilayered FPNN-MC structure is developed in a self-organizing way, using a structure learning procedure. The network's neurons are realized through fuzzy rule-based TSK systems, considered as generic fuzzy neuron classifiers (FNC's). Parent FNC's are combined to develop new higher-level descendant classifiers at the subsequent layer. Hence, sequential multistage decision is implemented, leading to improved classification results. To exploit the information acquired by FNC's at each layer and achieve an effective data flow, a fusion scheme is developed associated with a data reduction mechanism. Upon termination of the structure building, parameter learning is carried out using a genetic algorithm platform. A remarkable asset of the approach is that it resolves the feature selection task, providing the most relevant features of a problem. Simulation results on a well known classification problem indicate the efficiency of the proposed model","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133052376","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
Hardware Implementation of Traffic Controller using Fuzzy Expert System 基于模糊专家系统的交通控制器硬件实现
2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251175
M.S. Islam, M. S. Bhuyan, M. A. Azim, L.K. Teng, M. Othman
{"title":"Hardware Implementation of Traffic Controller using Fuzzy Expert System","authors":"M.S. Islam, M. S. Bhuyan, M. A. Azim, L.K. Teng, M. Othman","doi":"10.1109/ISEFS.2006.251175","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251175","url":null,"abstract":"This paper presents the design of traffic controller hardware using fuzzy expert system algorithm for traffic light controlling purpose. The process uses the knowledge base of a fuzzy system - rule base and parameters. This knowledge based system aspect makes the design more simple and efficient, especially when compared with traditional trial and error methods. A functional fuzzy traffic controller (FTC), which utilises fuzzy logic algorithm to achieve a smart and a flexible knowledge-based system in hardware design while achieving better efficiency in the traffic control and minimizing traffic jam occurrences at interchange on road area. We describe a hardware platform for evolving system by using knowledge base of a fuzzy system. To develop the system, the behaviour level of FTC algorithm has developed using very high speed integrated circuit (VHSIC) hardware description language (VHDL) under MAX+PLUS II CAD environment. The finite state machine (FSM) of the FTC has been coded in VHDL program for controlling the specific traffic flow application. Later on, the FPGA express (synthesis tool) has used to get a fully gate level synthesis architecture for the whole Fuzzy based hardware chip. The designed codes of the FTC have downloaded onto the UP1 FPGA (field programmable gate array) educational board (Altera FLEX10K) for verifying the FTC hardware chip functionality. The performance of an expert fuzzy system based chip for controlling traffic light is evaluated","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130527276","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 Formalism to Extract Fuzzy If-Then Rules from Numerical Data Using Genetic Algorithms 利用遗传算法从数值数据中提取模糊If-Then规则的形式化方法
2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251134
Zheng Pei
{"title":"A Formalism to Extract Fuzzy If-Then Rules from Numerical Data Using Genetic Algorithms","authors":"Zheng Pei","doi":"10.1109/ISEFS.2006.251134","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251134","url":null,"abstract":"In many applications knowledge required has to extract from a massive amount of numerical data. In this paper, extracting fuzzy if-then rules from numerical data is discussed. Due to The comprehensibility of fuzzy if-then rules is related to various factors. Our discussion is concentrated on simplicity of fuzzy rule-based systems, i.e., optimizing the number of input variables and the number of fuzzy if-then rules. Firstly, extracting fuzzy rule from numerical data is considered in decision information system, and confidence and support of fuzzy rule are obtained. Then, by encoding fuzzy partition and membership functions, selecting weighted mean of confidence and support of fuzzy rule as fitness function, optimizing the number of if-then rule and its inputs are formally discussed based on genetic algorithms (GAs)","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123525063","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
Design of LSI for crossover operation based on sequence pair 基于序列对的交叉操作集成电路设计
2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251171
M. Yoshikawa, H. Terai
{"title":"Design of LSI for crossover operation based on sequence pair","authors":"M. Yoshikawa, H. Terai","doi":"10.1109/ISEFS.2006.251171","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251171","url":null,"abstract":"The floorplanning problem, which is an essential design step in VLSI layout design, consists of determining the placement of rectangular modules as densely as possible. Many studies have been carried out on this problem using sequence pairs based on genetic algorithms (GAs). However, the GA-based method generally requires a great amount of computation time. Therefore, we propose the architecture for high speed floorplanning using a sequence pair based on GA. In this paper, the proposed architecture is implemented on LSI, and achieves high speed processing","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123634147","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
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