Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251145
E. Tawil, H. Hagras
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
{"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":"769 1","pages":"0"},"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}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251133
R. Precup, S. Preitl
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
{"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":"45 1","pages":"0"},"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}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251152
O. Cordón, E. Herrera-Viedma, M. Luque
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
{"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":"404 1","pages":"0"},"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}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251181
M. Santamouris, C. Georgakis, A. Niachou
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
本文介绍了一项广泛研究的结果，旨在开发和验证能够估计城市峡谷风速的替代数据驱动技术。由于输入数据的不确定性和对物理现象的描述不完整，使用确定性技术计算峡谷风速的精度较低。(C. Georgakis et al.， 2004)从七个城市峡谷收集的扩展实验数据已用于创建定义该现象的主要参数的数据库。利用模糊聚类技术，以惯性和重力为标准，建立了输入-输出数据的聚类。通过统计分析，估算出峡谷内最可能的风速和相应的输入值。因此，创建了一个简化的数据空间。这个简化的数据空间被用来开发四种数据驱动的预测模型。模型包括三维图形插值法、基于树的模型和线性回归模型。利用图形插值模型的结果，建立了模糊估计模型。所有方法都与实验数据进行了比较
{"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":"1 1","pages":"0"},"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}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251130
Nand Kishor, S. P. Singh, A. S. Raghuvanshi, P. Sharma
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
{"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":"1 1","pages":"0"},"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}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251135
E. Lima, F. Gomide, R. Ballini
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
{"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":"14 1","pages":"0"},"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}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251177
N. Mitrakis, J. Theocharis
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
{"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":"60 1","pages":"0"},"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}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251175
M.S. Islam, M. S. Bhuyan, M. A. Azim, L.K. Teng, M. Othman
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
本文提出了一种基于模糊专家系统算法的交通信号灯控制硬件设计。该过程使用了模糊系统的知识库——规则库和参数。与传统的试错法相比，这种基于知识的系统方面使设计更加简单和高效。一种功能性模糊交通控制器(FTC)，它利用模糊逻辑算法在硬件设计上实现了智能和灵活的基于知识的系统，同时提高了交通控制的效率，并最大限度地减少了道路交汇处的交通堵塞。利用模糊系统的知识库，描述了进化系统的硬件平台。为了开发该系统，在MAX+PLUS II CAD环境下，使用超高速集成电路(VHSIC)硬件描述语言(VHDL)开发了FTC算法的行为层。FTC的有限状态机(FSM)已在VHDL程序中编码，用于控制特定的交通流应用程序。随后，利用FPGA express(综合工具)得到了整个基于Fuzzy的硬件芯片的全门级综合体系结构。设计的FTC代码已下载到UP1 FPGA(现场可编程门阵列)教育板(Altera FLEX10K)上，用于验证FTC硬件芯片的功能。对基于专家模糊系统的红绿灯控制芯片的性能进行了评价
{"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":"1 1","pages":"0"},"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}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251134
Zheng Pei
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)
{"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":"17 1","pages":"0"},"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}
Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251171
M. Yoshikawa, H. Terai
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
{"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":"50 1","pages":"0"},"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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：