Pub Date : 2003-05-25DOI: 10.1109/FUZZ.2003.1206535
Zhenyuan Wang, Hai-Feng Guo
This paper gives a new genetic algorithm for nonlinear multiregression based on generalized Choquet integrals with respect to signed fuzzy measures. Unlike the previous work where the values of the signed fuzzy measure are determined by random search in a genetic algorithm with other regression coefficients together; in this new algorithm, they are determined algebraically and, therefore, its complexity is much lower than before.
{"title":"A new genetic algorithm for nonlinear multiregressions based on generalized Choquet integrals","authors":"Zhenyuan Wang, Hai-Feng Guo","doi":"10.1109/FUZZ.2003.1206535","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206535","url":null,"abstract":"This paper gives a new genetic algorithm for nonlinear multiregression based on generalized Choquet integrals with respect to signed fuzzy measures. Unlike the previous work where the values of the signed fuzzy measure are determined by random search in a genetic algorithm with other regression coefficients together; in this new algorithm, they are determined algebraically and, therefore, its complexity is much lower than before.","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":"126265529","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}
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.2003.1209377
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.2003.1206616
M. Graña, P. Sussner, G. Ritter
Autoassociative morphological memories (AMM) are a construct similar to hopfield autoassociatived memories defined on the (R, +, v, /spl and/) lattice algebra. Unlimited storage and perfect recall of noiseless real valued patterns has been proved for AMMs. However AMMs suffer from sensitivity to specific noise models, that can be characterized as erosive and dilative noise. On the other hand, spectral unmixing of hyperspectral images needs the prior definition of a set of endmembers, which correspond to material spectra lying on vertices of the minimum convex region covering the image data. These vertices can be characterized as morphologically independent patterns. We present a procedure based on the AMM noise sensitivity for endmember detection based on this characterization.
{"title":"Associative morphological memories for endmember determination in spectral unmixing","authors":"M. Graña, P. Sussner, G. Ritter","doi":"10.1109/FUZZ.2003.1206616","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206616","url":null,"abstract":"Autoassociative morphological memories (AMM) are a construct similar to hopfield autoassociatived memories defined on the (R, +, v, /spl and/) lattice algebra. Unlimited storage and perfect recall of noiseless real valued patterns has been proved for AMMs. However AMMs suffer from sensitivity to specific noise models, that can be characterized as erosive and dilative noise. On the other hand, spectral unmixing of hyperspectral images needs the prior definition of a set of endmembers, which correspond to material spectra lying on vertices of the minimum convex region covering the image data. These vertices can be characterized as morphologically independent patterns. We present a procedure based on the AMM noise sensitivity for endmember detection based on this characterization.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"69 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":"129559115","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.2003.1209438
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.2003.1209412
H. Allamehzadeh, J. Cheung
A new chattering-free Sliding Mode Fuzzy Controller (SMFC) with smooth control law is proposed for a class of nonlinear system. The proposed controller employs the concept of variable structure system with sliding mode or Sliding Mode Control (SMC), for design, and preserves the most fundamental property of conventional SMC that is robustness and invariance to model uncertainties. However, unlike the conventional sliding mode control, SMFC eliminates chattering problem through the concept input-output mapping factor and behave like a linear controller in the neighborhood of its sliding manifold. To demonstrate the superiority of SMFC over SMC, we conducted simulation studies on balancing an inverted pendulum at its upright position in the presence model uncertainties and external disturbances.
{"title":"Smooth response sliding mode fuzzy control with intrinsic boundary layer","authors":"H. Allamehzadeh, J. Cheung","doi":"10.1109/FUZZ.2003.1209412","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209412","url":null,"abstract":"A new chattering-free Sliding Mode Fuzzy Controller (SMFC) with smooth control law is proposed for a class of nonlinear system. The proposed controller employs the concept of variable structure system with sliding mode or Sliding Mode Control (SMC), for design, and preserves the most fundamental property of conventional SMC that is robustness and invariance to model uncertainties. However, unlike the conventional sliding mode control, SMFC eliminates chattering problem through the concept input-output mapping factor and behave like a linear controller in the neighborhood of its sliding manifold. To demonstrate the superiority of SMFC over SMC, we conducted simulation studies on balancing an inverted pendulum at its upright position in the presence model uncertainties and external disturbances.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"2 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":"128022554","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.2003.1206526
R. Chau, C. Yeh
An emerging requirement to sift through the increasing flood of multilingual text available electronically has led to the pressing demand for effective multilingual information filtering. In this paper, a content-based approach to multilingual information filtering is proposed. This approach is capable of screening and evaluating multilingual documents based on their semantic content. As such, relevant multilingual documents are disseminated according to their corresponding themes/topics to facilitate both efficient and effective content-based information access. The objective of alleviating users' burden of information overload is thus achieved. This approach is realized by incorporating fuzzy clustering and fuzzy inference techniques.
{"title":"Fuzzy multilingual information filtering","authors":"R. Chau, C. Yeh","doi":"10.1109/FUZZ.2003.1206526","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1206526","url":null,"abstract":"An emerging requirement to sift through the increasing flood of multilingual text available electronically has led to the pressing demand for effective multilingual information filtering. In this paper, a content-based approach to multilingual information filtering is proposed. This approach is capable of screening and evaluating multilingual documents based on their semantic content. As such, relevant multilingual documents are disseminated according to their corresponding themes/topics to facilitate both efficient and effective content-based information access. The objective of alleviating users' burden of information overload is thus achieved. This approach is realized by incorporating fuzzy clustering and fuzzy inference techniques.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"44 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":"116313009","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.2003.1209318
Zhiheng Huang, Q. Shen
Interpolative reasoning methods do not only help reduce the complexity of fuzzy models but also make inference in sparse-rule based systems possible. This paper presents an interpolative reasoning method by exploiting the center of gravity (COG) property of the fuzzy sets concerned. The method works by first constructing a new inference rule via manipulating two given adjacent rules, and then by using similarity information to convert the intermediate inference results into the final derived conclusion. Two transformation operations are introduced to support such reasoning, which allow the COG of a fuzzy set to remain unaltered before and after the transformation. Results of experimental comparisons are provided to reflect the success of this work.
{"title":"A new fuzzy interpolative reasoning method based on center of gravity","authors":"Zhiheng Huang, Q. Shen","doi":"10.1109/FUZZ.2003.1209318","DOIUrl":"https://doi.org/10.1109/FUZZ.2003.1209318","url":null,"abstract":"Interpolative reasoning methods do not only help reduce the complexity of fuzzy models but also make inference in sparse-rule based systems possible. This paper presents an interpolative reasoning method by exploiting the center of gravity (COG) property of the fuzzy sets concerned. The method works by first constructing a new inference rule via manipulating two given adjacent rules, and then by using similarity information to convert the intermediate inference results into the final derived conclusion. Two transformation operations are introduced to support such reasoning, which allow the COG of a fuzzy set to remain unaltered before and after the transformation. Results of experimental comparisons are provided to reflect the success of this work.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"8 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":"125513537","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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