Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1209438
S. Coppock, L. Mazlack
Clustering groups items together that are most similar to each other and sets those that are least similar into different clusters. Methods have been developed to cluster records in a data set that are of only qualitative or quantitative data. Data sets exist that contain a mix of qualitative (nominal and ordinal) and quantitative (discrete and continuous) data. Clustering records of mixed kinds of data is a difficult problem. A metric to measure the similarity between records of mixed data types is needed. Once a clustering is found, we do not know how to best evaluate the quality of the clustering when there is a mixture of data varieties.
{"title":"Soft multi-modal data fusion","authors":"S. Coppock, L. Mazlack","doi":"10.1109/FUZZ.2003.1209438","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209438","url":null,"abstract":"Clustering groups items together that are most similar to each other and sets those that are least similar into different clusters. Methods have been developed to cluster records in a data set that are of only qualitative or quantitative data. Data sets exist that contain a mix of qualitative (nominal and ordinal) and quantitative (discrete and continuous) data. Clustering records of mixed kinds of data is a difficult problem. A metric to measure the similarity between records of mixed data types is needed. Once a clustering is found, we do not know how to best evaluate the quality of the clustering when there is a mixture of data varieties.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129571799","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209386
F. J. Moreno-Velo, I. Baturone, S. Sánchez-Solano, A. Barriga
The crecient use of fuzzy systems in complex applications has motivated us to develop a new version of Xfuzzy, the design environment for fuzzy system created at the IMSE (Instituto de Microelectronica de Sevilla). This new version, Xfuzzy 3.0, offers the advantages of being enterely programmed in Java, and allows designing hierarchical rule bases that can interchange fuzzy or non fuzzy values as well as employ user-defined fuzzy connectives, linguistic hedges, membership functions, and defuzzification methods. Xfuzzy 3.0 integrates tools that facilitate the description, tuning, verification, and synthesis of complex fuzzy systems. This is illustrated in this paper with the design of a fuzzy controller to solve a parking problem.
{"title":"Rapid design of fuzzy systems with Xfuzzy","authors":"F. J. Moreno-Velo, I. Baturone, S. Sánchez-Solano, A. Barriga","doi":"10.1109/FUZZ.2003.1209386","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209386","url":null,"abstract":"The crecient use of fuzzy systems in complex applications has motivated us to develop a new version of Xfuzzy, the design environment for fuzzy system created at the IMSE (Instituto de Microelectronica de Sevilla). This new version, Xfuzzy 3.0, offers the advantages of being enterely programmed in Java, and allows designing hierarchical rule bases that can interchange fuzzy or non fuzzy values as well as employ user-defined fuzzy connectives, linguistic hedges, membership functions, and defuzzification methods. Xfuzzy 3.0 integrates tools that facilitate the description, tuning, verification, and synthesis of complex fuzzy systems. This is illustrated in this paper with the design of a fuzzy controller to solve a parking problem.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131061985","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209377
R. Langari, Jong-Seob Won
This paper proposes a "traffic situation awareness" driven intelligent agent for energy management of parallel hybrid vehicles. A coordinating device that governs energy flow in the powertrain is proposed based on the idea that driving environment (traffic situation) as well as the vehicle's mode of operation and the style of driver behavior directly affect fuel usage and pollutant emissions. For the realization of driving situation awareness, identification processes for roadway type is performed by extracting the driving information from the (past) driving data. Expert knowledge that characterizes the relationship between the driving situation and fuel consumption and emissions is implemented in the fuzzy torque distributor that performs intelligent decisionmaking for the torque distribution task. Charge sustenance operation is performed in the State-of-Charge (SOC) compensator to keep the level of the state of charge within prescribed levels. The mission of the energy management system, so called Intelligent Energy Management Agent (IEMA), is to enable the vehicle to be driven in an economically and environmentally friendly way while satisfying the driver's performance demand. Simulation work is carried out for the validation of proposed IEMA, and the results reveal its viability for energy management of a parallel hybrid vehicle.
提出了一种“交通态势感知”驱动的并联混合动力汽车能量管理智能体。基于驾驶环境(交通状况)以及车辆的操作方式和驾驶人的行为方式直接影响燃油使用和污染物排放的思想，提出了一种动力系统中能量流的协调装置。为了实现驾驶态势感知，通过从(过去)驾驶数据中提取驾驶信息，进行道路类型的识别过程。将表征驾驶情况与油耗、排放之间关系的专家知识运用到模糊分压器中，对分配任务进行智能决策。在荷电状态(SOC)补偿器中执行电荷维持操作，以保持荷电状态在规定的水平内。被称为智能能源管理代理(Intelligent energy management Agent, IEMA)的能源管理系统的使命是在满足驾驶员性能需求的同时，使车辆以经济、环保的方式行驶。通过仿真验证了该方法的有效性，结果表明该方法适用于并联混合动力汽车的能量管理。
{"title":"Integrated drive cycle analysis for fuzzy logic based energy management in hybrid vehicles","authors":"R. Langari, Jong-Seob Won","doi":"10.1109/FUZZ.2003.1209377","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209377","url":null,"abstract":"This paper proposes a \"traffic situation awareness\" driven intelligent agent for energy management of parallel hybrid vehicles. A coordinating device that governs energy flow in the powertrain is proposed based on the idea that driving environment (traffic situation) as well as the vehicle's mode of operation and the style of driver behavior directly affect fuel usage and pollutant emissions. For the realization of driving situation awareness, identification processes for roadway type is performed by extracting the driving information from the (past) driving data. Expert knowledge that characterizes the relationship between the driving situation and fuel consumption and emissions is implemented in the fuzzy torque distributor that performs intelligent decisionmaking for the torque distribution task. Charge sustenance operation is performed in the State-of-Charge (SOC) compensator to keep the level of the state of charge within prescribed levels. The mission of the energy management system, so called Intelligent Energy Management Agent (IEMA), is to enable the vehicle to be driven in an economically and environmentally friendly way while satisfying the driver's performance demand. Simulation work is carried out for the validation of proposed IEMA, and the results reveal its viability for energy management of a parallel hybrid vehicle.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129477513","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1206646
N. Watanabe
Some indices of fuzziness are introduced for providing helpful information in fuzzy clustering. These indices play an auxiliary role in fuzzy clustering and can be used for deciding the number of clusters by combining with another criterion. Numerical examples are given for demonstrating how these indices can be applied.
{"title":"Fuzziness indices for fuzzy clustering","authors":"N. Watanabe","doi":"10.1109/FUZZ.2003.1206646","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206646","url":null,"abstract":"Some indices of fuzziness are introduced for providing helpful information in fuzzy clustering. These indices play an auxiliary role in fuzzy clustering and can be used for deciding the number of clusters by combining with another criterion. Numerical examples are given for demonstrating how these indices can be applied.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129845227","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209354
R. Ballini
A novel learning algorithm for recurrent neurofuzzy networks is introduced in this paper. The core of the learning algorithm uses equality index as the performance measure to be optimized. Equality index is especially important because its properties reflect the fuzzy set-based structure of the neural network and nature of learning. Equality indexes are strongly tied with the properties of the fuzzy set theory and logic-based techniques. The neural network recurrent topology is built with fuzzy neuron units and performs neural processing consistent with fuzzy system methodology. Therefore neural processing and learning are fully embodied within fuzzy set theory. The performance recurrent neurofuzzy network is verified via examples of nonlinear systems modeling. Computational experiments show that the recurrent fuzzy neural models developed are simpler and that learning is faster than both, static neural and neural fuzzy networks and alternative recurrent fuzzy neural networks.
{"title":"Equality index and learning in recurrent fuzzy neural networks","authors":"R. Ballini","doi":"10.1109/FUZZ.2003.1209354","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209354","url":null,"abstract":"A novel learning algorithm for recurrent neurofuzzy networks is introduced in this paper. The core of the learning algorithm uses equality index as the performance measure to be optimized. Equality index is especially important because its properties reflect the fuzzy set-based structure of the neural network and nature of learning. Equality indexes are strongly tied with the properties of the fuzzy set theory and logic-based techniques. The neural network recurrent topology is built with fuzzy neuron units and performs neural processing consistent with fuzzy system methodology. Therefore neural processing and learning are fully embodied within fuzzy set theory. The performance recurrent neurofuzzy network is verified via examples of nonlinear systems modeling. Computational experiments show that the recurrent fuzzy neural models developed are simpler and that learning is faster than both, static neural and neural fuzzy networks and alternative recurrent fuzzy neural networks.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130955380","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1206592
T. Joronen
The development of fuzzy sets has led to computational theory of perceptions (CTP). This paper presents a philosophical study on fuzzy sets and fuzzy applications and aims towards a deeper understanding about them. Ludwig Wittgenstein's philosophy can be used to illustrate fuzzy sets. Relating to Wittgenstein's approach, some interesting studies on 'vagueness' appeared before the genesis of fuzzy sets in 1965. We introduce a simple meaning articulation paradigm (MAP) of human meaning processing and apply it to fuzzy applications. The MAP applied to two case studies on fuzzy optimization and on a fuzzy Web query shows that some problems exist in traditional approaches.
{"title":"A philosophical study on fuzzy sets and fuzzy applications","authors":"T. Joronen","doi":"10.1109/FUZZ.2003.1206592","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206592","url":null,"abstract":"The development of fuzzy sets has led to computational theory of perceptions (CTP). This paper presents a philosophical study on fuzzy sets and fuzzy applications and aims towards a deeper understanding about them. Ludwig Wittgenstein's philosophy can be used to illustrate fuzzy sets. Relating to Wittgenstein's approach, some interesting studies on 'vagueness' appeared before the genesis of fuzzy sets in 1965. We introduce a simple meaning articulation paradigm (MAP) of human meaning processing and apply it to fuzzy applications. The MAP applied to two case studies on fuzzy optimization and on a fuzzy Web query shows that some problems exist in traditional approaches.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128312601","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209455
Pei-Yi Hao, J. Chiang
Fuzziness must he considered in systems where human estimation is influential. A model of such a vague phenomenon might he represented as a fuzzy system equation which can he described by the fuzzy functions defined by Zadeh’s extension principle. In this paper, we incorporate the concept of fuzzy set theory into the support vector machine (SVM) regression. The parameters to he identified in SVM regression, such as the components within the weight vector and the bias term, are fuzzy numbers, and the desired outputs in training samples are also fuzzy numbers. This integration preserves the benefits of SVM regression model and fuzzy regression model, where the SVM learning theory characterizes properties of learning machines which enable them to generalize well the unseen data and the fuzzy set theory might he very useful for finding a fuzzy structure in an evaluation system.
{"title":"A fuzzy model of support vector machine regression","authors":"Pei-Yi Hao, J. Chiang","doi":"10.1109/FUZZ.2003.1209455","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209455","url":null,"abstract":"Fuzziness must he considered in systems where human estimation is influential. A model of such a vague phenomenon might he represented as a fuzzy system equation which can he described by the fuzzy functions defined by Zadeh’s extension principle. In this paper, we incorporate the concept of fuzzy set theory into the support vector machine (SVM) regression. The parameters to he identified in SVM regression, such as the components within the weight vector and the bias term, are fuzzy numbers, and the desired outputs in training samples are also fuzzy numbers. This integration preserves the benefits of SVM regression model and fuzzy regression model, where the SVM learning theory characterizes properties of learning machines which enable them to generalize well the unseen data and the fuzzy set theory might he very useful for finding a fuzzy structure in an evaluation system.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122859113","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209370
N. Kubota, M. Mihara
This paper deals with multi-objective behavior coordination of multiple robots interacting with a quasi-ecosystem which is composed of insects and plants. In this ecosystem, there co-exist plants and insects according to specific reproduction rules. In general, the inhabiting area of each species is localized owing to geographical, climatic, and ecological factors. This indicates the population density of each species in one area is different from another according to local environmental conditions. In this study. multiple robots are introduced in order to maintain the ecosystem. Each robot takes actions based on multi-objective behavior coordination integrating several action outputs. However, the robot must select its suitable area in order to adapt to the current state of the quasi-ecosystem that might change dynamically. In this paper, we discuss target selection for insect removing and plant reaping behaviors through several computer simulations in a dynamically changing environment.
{"title":"Multi-objective behavior coordination of multiple robots interacting with a dynamic environment","authors":"N. Kubota, M. Mihara","doi":"10.1109/FUZZ.2003.1209370","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209370","url":null,"abstract":"This paper deals with multi-objective behavior coordination of multiple robots interacting with a quasi-ecosystem which is composed of insects and plants. In this ecosystem, there co-exist plants and insects according to specific reproduction rules. In general, the inhabiting area of each species is localized owing to geographical, climatic, and ecological factors. This indicates the population density of each species in one area is different from another according to local environmental conditions. In this study. multiple robots are introduced in order to maintain the ecosystem. Each robot takes actions based on multi-objective behavior coordination integrating several action outputs. However, the robot must select its suitable area in order to adapt to the current state of the quasi-ecosystem that might change dynamically. In this paper, we discuss target selection for insect removing and plant reaping behaviors through several computer simulations in a dynamically changing environment.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122665506","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209403
Chiang-Cheng Chiang, Wen-Hao Wang
Based on the combination of the H/sup /spl infin// optimal control with fuzzy logic control and the simple adaptation laws, this paper presents a new and feasible design algorithm to synthesize a decentralized robust adaptive fuzzy controller which can easily tackle the output tracking control problem of large-scale nonlinear uncertain systems without the knowledge of the upper bounds on the norm of the uncertainties.
{"title":"Decentralized robust adaptive fuzzy controller for large-scale nonlinear uncertain systems","authors":"Chiang-Cheng Chiang, Wen-Hao Wang","doi":"10.1109/FUZZ.2003.1209403","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209403","url":null,"abstract":"Based on the combination of the H/sup /spl infin// optimal control with fuzzy logic control and the simple adaptation laws, this paper presents a new and feasible design algorithm to synthesize a decentralized robust adaptive fuzzy controller which can easily tackle the output tracking control problem of large-scale nonlinear uncertain systems without the knowledge of the upper bounds on the norm of the uncertainties.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122713418","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 : 2003-05-25DOI: 10.1109/FUZZ.2003.1209345
O. Castillo, P. Melin
We describe in this paper a new approach for plant monitoring and diagnostics using type-2 fuzzy logic and fractal theory. The concept of the fractal dimension is used to measure the complexity of the time series of relevant variables for the process. A set of type-2 fuzzy rules is used to represent the knowledge for monitoring the process. In the type-2 fuzzy rules, the fractal dimension is used as a linguistic variable to help in recognizing specific patterns in the measured data. The fuzzy-fractal approach has been applied before in problems of financial time series prediction and for other types of problems, but now it is proposed to the monitoring of plants using type-2 fuzzy logic. We also compare the results of the type-2 fuzzy logic approach with the results of using only a traditional type-1 approach. Experimental results show a significant improvement in the monitoring ability with the type-2 fuzzy logic approach.
{"title":"A new hybrid approach for plant monitoring and diagnostics using type-2 fuzzy logic and fractal theory","authors":"O. Castillo, P. Melin","doi":"10.1109/FUZZ.2003.1209345","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209345","url":null,"abstract":"We describe in this paper a new approach for plant monitoring and diagnostics using type-2 fuzzy logic and fractal theory. The concept of the fractal dimension is used to measure the complexity of the time series of relevant variables for the process. A set of type-2 fuzzy rules is used to represent the knowledge for monitoring the process. In the type-2 fuzzy rules, the fractal dimension is used as a linguistic variable to help in recognizing specific patterns in the measured data. The fuzzy-fractal approach has been applied before in problems of financial time series prediction and for other types of problems, but now it is proposed to the monitoring of plants using type-2 fuzzy logic. We also compare the results of the type-2 fuzzy logic approach with the results of using only a traditional type-1 approach. Experimental results show a significant improvement in the monitoring ability with the type-2 fuzzy logic approach.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125737816","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}
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