Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251170
D. Stavrakoudis, J. Theocharis
In this paper, a pipelined TSK-type recurrent fuzzy network (PTRFN) is proposed for nonlinear adaptive signal prediction. The PTRFN model consists of a number of modules interconnected in a cascaded form. The participating modules are implemented through recurrent fuzzy neural networks with internal dynamics. The structure of the modules is evolved sequentially from input-output data. The parameter learning task is accomplished using a gradient descent algorithm and the extended least squares method. The suggested predictor exhibits a series of attractive attributes, including effective spatial representation of the temporal patterns, enhanced memorizing capabilities, and low computational complexity. The nonlinear subsection of the predictor (PTRFN), followed by a linear subsection (a tapped delay-line filter) is tested on the adaptive speech prediction problem. Simulation results demonstrate that considerably better performance is obtained compared with other existing recurrent networks
{"title":"Nonlinear Adaptive Speech Prediction using a Pipelined Recurrent Fuzzy Network","authors":"D. Stavrakoudis, J. Theocharis","doi":"10.1109/ISEFS.2006.251170","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251170","url":null,"abstract":"In this paper, a pipelined TSK-type recurrent fuzzy network (PTRFN) is proposed for nonlinear adaptive signal prediction. The PTRFN model consists of a number of modules interconnected in a cascaded form. The participating modules are implemented through recurrent fuzzy neural networks with internal dynamics. The structure of the modules is evolved sequentially from input-output data. The parameter learning task is accomplished using a gradient descent algorithm and the extended least squares method. The suggested predictor exhibits a series of attractive attributes, including effective spatial representation of the temporal patterns, enhanced memorizing capabilities, and low computational complexity. The nonlinear subsection of the predictor (PTRFN), followed by a linear subsection (a tapped delay-line filter) is tested on the adaptive speech prediction problem. Simulation results demonstrate that considerably better performance is obtained compared with other existing recurrent networks","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":"125858169","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.2006.251151
I. Kallel, N. Baklouti, A. Alimi
Design of robot controller for motion planning, using fuzzy logic control, requires formulation of rules that are collectively responsible for necessary levels of intelligent behaviors. To ensure the model interpretability, this collection of rules can be naturally decomposed and efficiently implemented as a hierarchical fuzzy model. This paper describes how this can be done using hybrid hierarchical genetic fuzzy modeling. The idea is to combine, in a hierarchical design, "mapping" for sub-goal behavior (SGB), and "reactivity" for local avoiding obstacles behavior (LAOB), to have at the same time, an interpretable and precise communicating system for robot motion planning controller. The design of each fuzzy unit of the hierarchical model is automatically ensured by MAGAD-BFS method (multi-agent genetic algorithm for the design of beta fuzzy systems), promoting itself as an interpretability-accuracy trade-off. A proposed reduced version of generalized local Voronoi diagram (RGLVD) comes to guarantee a high degree of precision for robot motion to attempt destinations (sub-goals). Compared to the navigation using only fuzzy rules controller, the hybrid hierarchical model is more efficient in terms of saving time and optimizing path
{"title":"Accuracy Preserving Interpretability with Hybrid Hierarchical Genetic Fuzzy Modeling: Case of Motion Planning Robot Controller","authors":"I. Kallel, N. Baklouti, A. Alimi","doi":"10.1109/ISEFS.2006.251151","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251151","url":null,"abstract":"Design of robot controller for motion planning, using fuzzy logic control, requires formulation of rules that are collectively responsible for necessary levels of intelligent behaviors. To ensure the model interpretability, this collection of rules can be naturally decomposed and efficiently implemented as a hierarchical fuzzy model. This paper describes how this can be done using hybrid hierarchical genetic fuzzy modeling. The idea is to combine, in a hierarchical design, \"mapping\" for sub-goal behavior (SGB), and \"reactivity\" for local avoiding obstacles behavior (LAOB), to have at the same time, an interpretable and precise communicating system for robot motion planning controller. The design of each fuzzy unit of the hierarchical model is automatically ensured by MAGAD-BFS method (multi-agent genetic algorithm for the design of beta fuzzy systems), promoting itself as an interpretability-accuracy trade-off. A proposed reduced version of generalized local Voronoi diagram (RGLVD) comes to guarantee a high degree of precision for robot motion to attempt destinations (sub-goals). Compared to the navigation using only fuzzy rules controller, the hybrid hierarchical model is more efficient in terms of saving time and optimizing path","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":"123546895","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.2006.251138
R. Almeida, J. Sousa
The identification of fuzzy models for classification is a very complex task. Often, real world databases have a large number of features and the most relevant ones must be chosen. Recently, a new automatic feature selection for classification problems was proposed to construct compact fuzzy classification models. This technique used the classical fuzzy c-means algorithm. However, other fuzzy clustering algorithms, such as possibilistic c-means, fuzzy possibilistic c-means or possibilistic fuzzy c-means can be used to cluster the data. An open topic of research is what clustering algorithms can be used to derive fuzzy models for classification. This paper addresses this topic, by comparing fuzzy clustering algorithms in terms of computational efficiency and accuracy in classification problems. The algorithms were tested in well-known data sets: iris plant, wine, hepatitis, breast cancer and in a difficult real-world problem: the prediction of bankruptcy
{"title":"Comparison of fuzzy clustering algorithms for classification","authors":"R. Almeida, J. Sousa","doi":"10.1109/ISEFS.2006.251138","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251138","url":null,"abstract":"The identification of fuzzy models for classification is a very complex task. Often, real world databases have a large number of features and the most relevant ones must be chosen. Recently, a new automatic feature selection for classification problems was proposed to construct compact fuzzy classification models. This technique used the classical fuzzy c-means algorithm. However, other fuzzy clustering algorithms, such as possibilistic c-means, fuzzy possibilistic c-means or possibilistic fuzzy c-means can be used to cluster the data. An open topic of research is what clustering algorithms can be used to derive fuzzy models for classification. This paper addresses this topic, by comparing fuzzy clustering algorithms in terms of computational efficiency and accuracy in classification problems. The algorithms were tested in well-known data sets: iris plant, wine, hepatitis, breast cancer and in a difficult real-world problem: the prediction of bankruptcy","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":"123553598","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.2006.251156
L. Sánchez, Inés Couso, Jorge Casillas
When questionnaires are designed, each factor under study can be assigned a set of different items. The answers to these questions must be merged in order to obtain the level of that input. Therefore, it is typical for data acquired from questionnaires that each of the inputs and outputs are not numbers, but sets of values. In this paper, we represent the information contained in such a set of values by means of a fuzzy number. A fuzzy statistics-based interpretation of the semantic of a fuzzy set is used for this purpose, as we consider that this fuzzy number is a nested family of confidence intervals for the value of the variable. The accuracy of the model is expressed by means of an interval-valued function, derived from a definition of the variance of a fuzzy random variable. A multicriteria genetic learning algorithm, able to optimize this interval-valued function, is proposed. As an example of the application of this algorithm, a practical problem of modeling in marketing is solved
{"title":"A Multiobjective Genetic Fuzzy System with Imprecise Probability Fitness for Vague Data","authors":"L. Sánchez, Inés Couso, Jorge Casillas","doi":"10.1109/ISEFS.2006.251156","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251156","url":null,"abstract":"When questionnaires are designed, each factor under study can be assigned a set of different items. The answers to these questions must be merged in order to obtain the level of that input. Therefore, it is typical for data acquired from questionnaires that each of the inputs and outputs are not numbers, but sets of values. In this paper, we represent the information contained in such a set of values by means of a fuzzy number. A fuzzy statistics-based interpretation of the semantic of a fuzzy set is used for this purpose, as we consider that this fuzzy number is a nested family of confidence intervals for the value of the variable. The accuracy of the model is expressed by means of an interval-valued function, derived from a definition of the variance of a fuzzy random variable. A multicriteria genetic learning algorithm, able to optimize this interval-valued function, is proposed. As an example of the application of this algorithm, a practical problem of modeling in marketing is solved","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":"122461106","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.2006.251155
F. Hoffmann, S. Holemann
The utility of evolutionary algorithms for direct optimization of real processes or complex simulations is often limited by the large number of required fitness evaluations. Model assisted evolutionary algorithms economize on actual fitness evaluations by partially selecting individuals on the basis of a computationally less complex fitness model. We propose a novel model management scheme to regulate the number of preselected individuals to achieve optimal evolutionary progress with a minimal number of fitness evaluations. The number of preselected individuals is adapted to the model quality expressed by its ability to correctly predict the best individuals. The method achieves a substantial reduction of fitness evaluations on a set of benchmarks not only in comparison to a standard evolution strategy but also with respect to other model assisted optimization schemes
{"title":"Controlled Model Assisted Evolution Strategy with Adaptive Preselection","authors":"F. Hoffmann, S. Holemann","doi":"10.1109/ISEFS.2006.251155","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251155","url":null,"abstract":"The utility of evolutionary algorithms for direct optimization of real processes or complex simulations is often limited by the large number of required fitness evaluations. Model assisted evolutionary algorithms economize on actual fitness evaluations by partially selecting individuals on the basis of a computationally less complex fitness model. We propose a novel model management scheme to regulate the number of preselected individuals to achieve optimal evolutionary progress with a minimal number of fitness evaluations. The number of preselected individuals is adapted to the model quality expressed by its ability to correctly predict the best individuals. The method achieves a substantial reduction of fitness evaluations on a set of benchmarks not only in comparison to a standard evolution strategy but also with respect to other model assisted optimization schemes","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":"116039232","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.2006.251168
K. Valavanis
A general fuzzy logic based framework along with its application specific modifications is discussed to support, evaluate and justify the proposed perspective to unmanned vehicle autonomous navigation and control. Experimental and simulation results are included to validate and support implemented techniques and approaches to ground, aerial and underwater vehicles. A comparative study of classical and soft computing based controllers, designed to control small unmanned helicopters under hovering and cruising offers additional information related to the claim that fuzzy logic controllers may be implemented successfully when such helicopters perform non-aggressive flight patterns. The paper contribution is straight forward: it provides evidence of the usefulness and applicability of fuzzy logic as a viable alternative to using analytic approaches, and as a modeling tool that deals with imprecision and uncertainty
{"title":"Unmanned Vehcle Navigation and Control: A Fuzzy Logic Perspective","authors":"K. Valavanis","doi":"10.1109/ISEFS.2006.251168","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251168","url":null,"abstract":"A general fuzzy logic based framework along with its application specific modifications is discussed to support, evaluate and justify the proposed perspective to unmanned vehicle autonomous navigation and control. Experimental and simulation results are included to validate and support implemented techniques and approaches to ground, aerial and underwater vehicles. A comparative study of classical and soft computing based controllers, designed to control small unmanned helicopters under hovering and cruising offers additional information related to the claim that fuzzy logic controllers may be implemented successfully when such helicopters perform non-aggressive flight patterns. The paper contribution is straight forward: it provides evidence of the usefulness and applicability of fuzzy logic as a viable alternative to using analytic approaches, and as a modeling tool that deals with imprecision and uncertainty","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":"116473332","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.2006.251154
J. V. Ramos, A. Dourado
On-line implementation of mechanisms for merging membership functions and rule base simplification are studied in order to improve the interpretability of the eTS fuzzy models. This allows the minimization of redundancy and complexity of the models that may arrive during its development, increasing transparency (human interpretability). The on-line learning technique used is the evolving first-order Takagi-Sugeno (eTS) fuzzy models with rule spanned. A four rule fuzzy system is obtained for the Auto-Mpg benchmark data set with acceptable accuracy
{"title":"Pruning for interpretability of large spanned eTS","authors":"J. V. Ramos, A. Dourado","doi":"10.1109/ISEFS.2006.251154","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251154","url":null,"abstract":"On-line implementation of mechanisms for merging membership functions and rule base simplification are studied in order to improve the interpretability of the eTS fuzzy models. This allows the minimization of redundancy and complexity of the models that may arrive during its development, increasing transparency (human interpretability). The on-line learning technique used is the evolving first-order Takagi-Sugeno (eTS) fuzzy models with rule spanned. A four rule fuzzy system is obtained for the Auto-Mpg benchmark data set with acceptable accuracy","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":"114783923","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.2006.251182
J. E. M. Expósito, S. G. Galán, Nicolas Ruiz Reyes, P. V. Candeas, F. Pena
Automatic speech/music discrimination has become a research topic of interest in the last years. This paper presents a new approach for speech/music discrimination, which is based on an expert system that incorporates fuzzy rules into its knowledge base. The proposed scheme consists of three stages: 1) features extraction, 2) audio signal classification, and 3) selection of the best audio coder every 23 ms. The fuzzy expert system improves the accuracy rate of a GMM classifier when included into the classification stage. In order to select the best audio coder, the expert system takes information of the current and past frames into account. It is important to emphasize that the low computational cost of the proposed approach makes it feasible for real time applications
{"title":"Expert system for intelligent audio codification based in speech/music discrimination","authors":"J. E. M. Expósito, S. G. Galán, Nicolas Ruiz Reyes, P. V. Candeas, F. Pena","doi":"10.1109/ISEFS.2006.251182","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251182","url":null,"abstract":"Automatic speech/music discrimination has become a research topic of interest in the last years. This paper presents a new approach for speech/music discrimination, which is based on an expert system that incorporates fuzzy rules into its knowledge base. The proposed scheme consists of three stages: 1) features extraction, 2) audio signal classification, and 3) selection of the best audio coder every 23 ms. The fuzzy expert system improves the accuracy rate of a GMM classifier when included into the classification stage. In order to select the best audio coder, the expert system takes information of the current and past frames into account. It is important to emphasize that the low computational cost of the proposed approach makes it feasible for real time applications","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":"116757015","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.2006.251172
K. Tsutsumi, Y. Omori, K. Sasaki
The purpose of this study is to develop an optimum design method for roof shapes that satisfy the conditions of both beauty and dynamics by using a GA (genetic algorithm). The plane form of the building is a rectangle, and the design target is a concrete roof that covers this plane. The roof shapes were evaluated from the viewpoints of stress values and beauty. The stress values were obtained by FEM (finite element method) analysis. Additionally, the relationship between the roof shapes and beauty was investigated using questionnaires based on computer graphics. These results were analyzed to formulate KANSEI evaluation rules that incorporated the beauty and the elements of form. By using these rules, the degree of beauty of the roof shapes created using the GA could be estimated. A synthetic evaluation value was estimated from the two evaluation values adopted in this study. The roof shape that exhibits the lowest synthetic evaluation value was created by the genetic operation
{"title":"Roof Shape Generation Method for Buildings Using KANSEI Evaluation Rules","authors":"K. Tsutsumi, Y. Omori, K. Sasaki","doi":"10.1109/ISEFS.2006.251172","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251172","url":null,"abstract":"The purpose of this study is to develop an optimum design method for roof shapes that satisfy the conditions of both beauty and dynamics by using a GA (genetic algorithm). The plane form of the building is a rectangle, and the design target is a concrete roof that covers this plane. The roof shapes were evaluated from the viewpoints of stress values and beauty. The stress values were obtained by FEM (finite element method) analysis. Additionally, the relationship between the roof shapes and beauty was investigated using questionnaires based on computer graphics. These results were analyzed to formulate KANSEI evaluation rules that incorporated the beauty and the elements of form. By using these rules, the degree of beauty of the roof shapes created using the GA could be estimated. A synthetic evaluation value was estimated from the two evaluation values adopted in this study. The roof shape that exhibits the lowest synthetic evaluation value was created by the genetic operation","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":"122551417","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.2006.251166
H. Hagras, F. Doctor, A. López, V. Callaghan
This paper presents an overview of our work to produce type-2 fuzzy agents that can realize an intelligent ambience in everyday environments to form ambient intelligent environments (AIEs). The agents are embedded in the user environment where they learn the user behavior in a non intrusive mode and control the environment on the user behalf to realize the intelligent ambience. Type-2 fuzzy systems are able to handle the different sources of uncertainty and imprecision encountered in AIEs to give a very good response. However, there is a need to evolve the type-2 agents by evolving the type-2 membership functions (MFs) and rules in a life long learning mode to handle and accommodate for the uncertainties associated with the long term operations and the changing environmental conditions and user preferences. This paper presents an overview of the evolving type-2 agents which are evaluated in real world test beds for intelligent environments
{"title":"Evolving Type-2 Fuzzy Agents for Ambient Intelligent Environments","authors":"H. Hagras, F. Doctor, A. López, V. Callaghan","doi":"10.1109/ISEFS.2006.251166","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251166","url":null,"abstract":"This paper presents an overview of our work to produce type-2 fuzzy agents that can realize an intelligent ambience in everyday environments to form ambient intelligent environments (AIEs). The agents are embedded in the user environment where they learn the user behavior in a non intrusive mode and control the environment on the user behalf to realize the intelligent ambience. Type-2 fuzzy systems are able to handle the different sources of uncertainty and imprecision encountered in AIEs to give a very good response. However, there is a need to evolve the type-2 agents by evolving the type-2 membership functions (MFs) and rules in a life long learning mode to handle and accommodate for the uncertainties associated with the long term operations and the changing environmental conditions and user preferences. This paper presents an overview of the evolving type-2 agents which are evaluated in real world test beds for intelligent environments","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":"130914685","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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