Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295536
Corrado Mencar, A. Consiglio, A. Fanelli
In this paper we present an approach for extracting interpretable information granules for classification. The approach, called DCγ (double clustering with genetic algorithms) is based on two clustering steps. The first step uses LVQ1 to identify cluster prototypes in the multidimensional data space so as to represent hidden relationships among data. In the second step a genetic algorithm is applied to the projections of these prototypes with the objective of finding a minimal number of fuzzy information granules that verify some interpretability constraints. The key feature of DCγ is the efficiency of the minimization process carried out in the second step. Experimental results on two medical diagnosis problems show the effectiveness of the proposed approach in terms of accuracy, interpretability and efficiency.
{"title":"DCγ : Interpretable Granulation of Data through GA-based Double Clustering","authors":"Corrado Mencar, A. Consiglio, A. Fanelli","doi":"10.1109/FUZZY.2007.4295536","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295536","url":null,"abstract":"In this paper we present an approach for extracting interpretable information granules for classification. The approach, called DCγ (double clustering with genetic algorithms) is based on two clustering steps. The first step uses LVQ1 to identify cluster prototypes in the multidimensional data space so as to represent hidden relationships among data. In the second step a genetic algorithm is applied to the projections of these prototypes with the objective of finding a minimal number of fuzzy information granules that verify some interpretability constraints. The key feature of DCγ is the efficiency of the minimization process carried out in the second step. Experimental results on two medical diagnosis problems show the effectiveness of the proposed approach in terms of accuracy, interpretability and efficiency.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134188221","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295502
E. Herrera-López, Bernardino Castillo, Jesús Ramírez, E. Ferreira
The purpose of this work is to design an exact fuzzy observer for a bioprocess switching between two different metabolic states. A fed-batch baker's yeast culture is modeled by two sub-models: a respiro-fermentative state with ethanol production and a respirative state with ethanol consumption. An exact fuzzy observer model using sector nonlinearity was built for both nonlinear models; the observer gains were designed using Linear Matrix Inequalities (LMI's). The observer dynamics shows a very good tracking behavior with respect of the states of the switching partial models. The observer premise variables depend on the state variables estimated by the fuzzy observer.
{"title":"Exact Fuzzy Observer for a Baker's Yeast Fed-Batch Fermentation Process","authors":"E. Herrera-López, Bernardino Castillo, Jesús Ramírez, E. Ferreira","doi":"10.1109/FUZZY.2007.4295502","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295502","url":null,"abstract":"The purpose of this work is to design an exact fuzzy observer for a bioprocess switching between two different metabolic states. A fed-batch baker's yeast culture is modeled by two sub-models: a respiro-fermentative state with ethanol production and a respirative state with ethanol consumption. An exact fuzzy observer model using sector nonlinearity was built for both nonlinear models; the observer gains were designed using Linear Matrix Inequalities (LMI's). The observer dynamics shows a very good tracking behavior with respect of the states of the switching partial models. The observer premise variables depend on the state variables estimated by the fuzzy observer.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"274 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134273089","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295433
Yueh-Hsiang Chen, Ru-Jen Chao
E-learning has gradually become more and more important nowadays due to its advantages: (1) learning courses without constraints of time and place through asynchronous distance learning (2) saving training cost for enterprises. Official organizations, schools, and businesses invest a lot of time, money, and efforts in e-learning. A variety of e-learning materials were generated under such situations. This study is to inspect the affecting factors of e-learning material and apply consistent fuzzy linguistic preference relations to evaluate these factors. The evaluation can offer the information of decision-making to the e-learning material designers, especially with the constraint of time and money.
{"title":"Applying Consistent Fuzzy Linguistic Preference Relations to Evaluation of E-Learning Material Design","authors":"Yueh-Hsiang Chen, Ru-Jen Chao","doi":"10.1109/FUZZY.2007.4295433","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295433","url":null,"abstract":"E-learning has gradually become more and more important nowadays due to its advantages: (1) learning courses without constraints of time and place through asynchronous distance learning (2) saving training cost for enterprises. Official organizations, schools, and businesses invest a lot of time, money, and efforts in e-learning. A variety of e-learning materials were generated under such situations. This study is to inspect the affecting factors of e-learning material and apply consistent fuzzy linguistic preference relations to evaluate these factors. The evaluation can offer the information of decision-making to the e-learning material designers, especially with the constraint of time and money.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133041850","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295550
A. Hajiha, J. Jassbi, S. Khanmohammadi
Choosing suitable individuals for different positions in an organization has always been one of the most important concerns of management scientists. Several mathematical models have been used to quantify the comparable merits of individuals for different jobs. These are generally developed in a deterministic environment and deal with precise data while evaluating the extent of "merit" falls under a fuzzy environment with completely nonlinear relations between merits and behavioral features of employers. This paper deals with assigning individuals to jobs through designing a fuzzy model as an expert decision support system. To design such model, experimental knowledge of experts is benefited to define the rules. The extreme conditions are used to facilitate and to increase the precisions of experts' judgments. Then through evaluating individuals' scores and applying the obtained results to the fuzzy rule base, fuzzy merits are obtained for different individuals. Finally the linear assignment technique is applied to use the defuzzified merits to achieve an optimal job assignment. An auto after sales services company is considered as a case study.
{"title":"A Fuzzy Expert Decision Support System for Job Assignment","authors":"A. Hajiha, J. Jassbi, S. Khanmohammadi","doi":"10.1109/FUZZY.2007.4295550","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295550","url":null,"abstract":"Choosing suitable individuals for different positions in an organization has always been one of the most important concerns of management scientists. Several mathematical models have been used to quantify the comparable merits of individuals for different jobs. These are generally developed in a deterministic environment and deal with precise data while evaluating the extent of \"merit\" falls under a fuzzy environment with completely nonlinear relations between merits and behavioral features of employers. This paper deals with assigning individuals to jobs through designing a fuzzy model as an expert decision support system. To design such model, experimental knowledge of experts is benefited to define the rules. The extreme conditions are used to facilitate and to increase the precisions of experts' judgments. Then through evaluating individuals' scores and applying the obtained results to the fuzzy rule base, fuzzy merits are obtained for different individuals. Finally the linear assignment technique is applied to use the defuzzified merits to achieve an optimal job assignment. An auto after sales services company is considered as a case study.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"79 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133089392","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295404
Qingyong Li, Zhiping Shi, Siwei Luo
In order to improve the performance of content-based image retrieval (CBIR) systems, the 'semantic gap' between the low-level visual features and the high-level semantic features attracts more and more research interest. We propose an approach to describe and to extract the fuzzy color semantics. According to human color perception model, we utilize the linguistic variable to describe the image color semantics, so it becomes possible to depict the image in linguistic expression such as mostly red. Furthermore, we apply the feedforward neural network to model the vagueness of human color perception and to extract the fuzzy semantic feature vector. Our experiments show that the color semantic features have good accordance with the human perception, and also have good retrieval performance. In some extent, our approach shows the potential to reduce the semantic gap in CBIR.
{"title":"Image Retrieval Based on Fuzzy Color Semantics","authors":"Qingyong Li, Zhiping Shi, Siwei Luo","doi":"10.1109/FUZZY.2007.4295404","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295404","url":null,"abstract":"In order to improve the performance of content-based image retrieval (CBIR) systems, the 'semantic gap' between the low-level visual features and the high-level semantic features attracts more and more research interest. We propose an approach to describe and to extract the fuzzy color semantics. According to human color perception model, we utilize the linguistic variable to describe the image color semantics, so it becomes possible to depict the image in linguistic expression such as mostly red. Furthermore, we apply the feedforward neural network to model the vagueness of human color perception and to extract the fuzzy semantic feature vector. Our experiments show that the color semantic features have good accordance with the human perception, and also have good retrieval performance. In some extent, our approach shows the potential to reduce the semantic gap in CBIR.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114668833","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295355
Sarah Greenfield, R. John
The inferencing stage of a type-2 fuzzy inferencing system is driven by join and meet operations. As conventionally implemented these algorithms are computationally complex. This article introduces optimised implementations for these operations. These alternative procedures pre-suppose that the grid method of discretisation is adopted. Time comparisons between the alternative and conventional implementations have been undertaken, for join and meet operations coded firstly in isolation, and secondly, within a full prototype type-2 fuzzy inferencing system. The experimental results show that the optimisation affords marked time reduction, particularly under finer discretisation.
{"title":"Optimised Generalised Type-2 Join and Meet Operations","authors":"Sarah Greenfield, R. John","doi":"10.1109/FUZZY.2007.4295355","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295355","url":null,"abstract":"The inferencing stage of a type-2 fuzzy inferencing system is driven by join and meet operations. As conventionally implemented these algorithms are computationally complex. This article introduces optimised implementations for these operations. These alternative procedures pre-suppose that the grid method of discretisation is adopted. Time comparisons between the alternative and conventional implementations have been undertaken, for join and meet operations coded firstly in isolation, and secondly, within a full prototype type-2 fuzzy inferencing system. The experimental results show that the optimisation affords marked time reduction, particularly under finer discretisation.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114684135","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295332
P. Mastorocostas
A recurrent fuzzy neural network with internal feedback is suggested in this paper. The network is entitled Dynamic Block-Diagonal Fuzzy Neural Network (DBD-FNN), and constitutes a generalized Takagi-Sugeno-Kang fuzzy system, where the consequent parts of the fuzzy rules are small Block-Diagonal Recurrent Neural Networks. The proposed model is applied to a benchmark problem, where a dynamic system is to be identified. A comparative analysis with a series of recurrent fuzzy and neural models is conducted, highlighting the modeling characteristics of DBD-FNN.
{"title":"A Block-Diagonal Recurrent Fuzzy Neural Network for Dynamic System Identification","authors":"P. Mastorocostas","doi":"10.1109/FUZZY.2007.4295332","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295332","url":null,"abstract":"A recurrent fuzzy neural network with internal feedback is suggested in this paper. The network is entitled Dynamic Block-Diagonal Fuzzy Neural Network (DBD-FNN), and constitutes a generalized Takagi-Sugeno-Kang fuzzy system, where the consequent parts of the fuzzy rules are small Block-Diagonal Recurrent Neural Networks. The proposed model is applied to a benchmark problem, where a dynamic system is to be identified. A comparative analysis with a series of recurrent fuzzy and neural models is conducted, highlighting the modeling characteristics of DBD-FNN.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114453506","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295674
E. Gelenbe
We study gene regulatory networks that include the effect of multiple binary interactions, when various genes can activate or inhibit other genes, as well as of ternary interactions where two genes jointly affect the level of activity of a third gene. We also prove that Boolean identities for the activation state of a gene in terms of the activation level of another set of genes, can be represented, so that the overall probability of activation of any gene in the network can be computed in the presence of logical dependencies, and excitatory-inhibitory interactions between genes.
{"title":"Analytical Solution of Gene Regulatory Networks","authors":"E. Gelenbe","doi":"10.1109/FUZZY.2007.4295674","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295674","url":null,"abstract":"We study gene regulatory networks that include the effect of multiple binary interactions, when various genes can activate or inhibit other genes, as well as of ternary interactions where two genes jointly affect the level of activity of a third gene. We also prove that Boolean identities for the activation state of a gene in terms of the activation level of another set of genes, can be represented, so that the overall probability of activation of any gene in the network can be computed in the presence of logical dependencies, and excitatory-inhibitory interactions between genes.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116142496","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295357
Tiejun Zhang, G. Feng, J. Lu
In this paper, a fuzzy affine model, which is more capable of representing strongly nonlinear dynamics, is used for predictive controller design. Based on piecewise quadratic Lyapunov functions, the proposed fuzzy affine model predictive control approach can ensure both the closed-loop system stability and the satisfactory transient control performance even under input and state constraints. With the help of partitioned degenerate ellipsoids and S-procedure, the large terminal invariant set of a fuzzy affine system can be achieved offline by solving a convex semi-definite programming problem subject to some linear matrix inequalities, rather than the non-convex bilinear matrix inequalities as in conventional fuzzy affine model based control. Then with the associated terminal cost, the resulting online open-loop predictive control approach can be formulated as a standard quadratic programming problem, which is readily solvable. Simulation results have demonstrated the performance of the proposed approach.
{"title":"Stable Model Predictive Control of Fuzzy Affine Systems with Input and State Constraints","authors":"Tiejun Zhang, G. Feng, J. Lu","doi":"10.1109/FUZZY.2007.4295357","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295357","url":null,"abstract":"In this paper, a fuzzy affine model, which is more capable of representing strongly nonlinear dynamics, is used for predictive controller design. Based on piecewise quadratic Lyapunov functions, the proposed fuzzy affine model predictive control approach can ensure both the closed-loop system stability and the satisfactory transient control performance even under input and state constraints. With the help of partitioned degenerate ellipsoids and S-procedure, the large terminal invariant set of a fuzzy affine system can be achieved offline by solving a convex semi-definite programming problem subject to some linear matrix inequalities, rather than the non-convex bilinear matrix inequalities as in conventional fuzzy affine model based control. Then with the associated terminal cost, the resulting online open-loop predictive control approach can be formulated as a standard quadratic programming problem, which is readily solvable. Simulation results have demonstrated the performance of the proposed approach.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114987968","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295367
V. Barra, V. Delouille, J. Hochedez
Accurate means of quantifying the respective contributions of different structures to the solar irradiance is now a key issue in Solar Physics, with implications to Sun-Earth relationships and space weather study. In this paper, we propose a three-step fusion scheme, that allows to aggregate (17.1 nm, 19.5 nm) data stemming from the solar EIT instrument onboard the SoHO mission, and that is flexible enough to allow the integration of other type of information. The method is based on both a spatially constrained possibilistic clustering algorithm and a context dependent fusion operator. It aggregates the complementary and redundant information coming from the input sources. The results obtained on a 9-year dataset are consistent with those found in the solar physics literature. Unlike previous algorithms used in solar physics, our method has the ability to add further heterogeneous sources and sensors (e.g. human knowledge, images in other bandpasses, ratio of images) to the process, in order to postpone the decision step (here the segmentation of structures of interest) until sufficient information is available.
{"title":"Segmentation of Extreme Ultraviolet Solar Images using a Multispectral Data Fusion Process","authors":"V. Barra, V. Delouille, J. Hochedez","doi":"10.1109/FUZZY.2007.4295367","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295367","url":null,"abstract":"Accurate means of quantifying the respective contributions of different structures to the solar irradiance is now a key issue in Solar Physics, with implications to Sun-Earth relationships and space weather study. In this paper, we propose a three-step fusion scheme, that allows to aggregate (17.1 nm, 19.5 nm) data stemming from the solar EIT instrument onboard the SoHO mission, and that is flexible enough to allow the integration of other type of information. The method is based on both a spatially constrained possibilistic clustering algorithm and a context dependent fusion operator. It aggregates the complementary and redundant information coming from the input sources. The results obtained on a 9-year dataset are consistent with those found in the solar physics literature. Unlike previous algorithms used in solar physics, our method has the ability to add further heterogeneous sources and sensors (e.g. human knowledge, images in other bandpasses, ratio of images) to the process, in order to postpone the decision step (here the segmentation of structures of interest) until sufficient information is available.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115171296","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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