Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295504
Yian-Kui Liu, X. Dai
Based on mean chance theory, this paper presents a new class of two-stage fuzzy random minimum risk problem (FRMRP), and discusses its deterministic equivalent programming problem. Because the FRMRPs include fuzzy random variable parameters with infinite supports, they are inherently infinite-dimensional optimization problems that can rarely be solved directly. Therefore, an approximation approach to the original two-stage FRMRPs is proposed, which results in finite-dimensional approximating two-stage FRMRPs. After that, the paper deals with the convergence of the objective value of the approximating two-stage FRMRP to that of the original two-stage FRMRP.
{"title":"Minimum-Risk Criteria in Two-Stage Fuzzy Random Programming","authors":"Yian-Kui Liu, X. Dai","doi":"10.1109/FUZZY.2007.4295504","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295504","url":null,"abstract":"Based on mean chance theory, this paper presents a new class of two-stage fuzzy random minimum risk problem (FRMRP), and discusses its deterministic equivalent programming problem. Because the FRMRPs include fuzzy random variable parameters with infinite supports, they are inherently infinite-dimensional optimization problems that can rarely be solved directly. Therefore, an approximation approach to the original two-stage FRMRPs is proposed, which results in finite-dimensional approximating two-stage FRMRPs. After that, the paper deals with the convergence of the objective value of the approximating two-stage FRMRP to that of the original two-stage FRMRP.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"263 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":"115282353","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.2007.4295479
P. Stewart, D. Gladwin, M. Parr, J. Stewart
An investigation is made in this paper of the possibility of enhancing the performance of controllers of unstable systems while retaining safety critical function. In this case, a general dynamics F16 fighter is considered in simulation. A fuzzy logic controller is designed and its membership functions tuned by multiobjective genetic algorithms in order to design an augmented flight controller with enhanced manouverability which still retains safety critical operation. The controller is assessed in terms of pilot effort and thus reduction of pilot fatigue. The controller is incorporated into a six degree of freedom real-time flight simulator, and flight tested by a qualified pilot instructor.
{"title":"A Multiobjective G.A./Fuzzy Logic augmented flight controller for an F16 aircraft","authors":"P. Stewart, D. Gladwin, M. Parr, J. Stewart","doi":"10.1109/FUZZY.2007.4295479","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295479","url":null,"abstract":"An investigation is made in this paper of the possibility of enhancing the performance of controllers of unstable systems while retaining safety critical function. In this case, a general dynamics F16 fighter is considered in simulation. A fuzzy logic controller is designed and its membership functions tuned by multiobjective genetic algorithms in order to design an augmented flight controller with enhanced manouverability which still retains safety critical operation. The controller is assessed in terms of pilot effort and thus reduction of pilot fatigue. The controller is incorporated into a six degree of freedom real-time flight simulator, and flight tested by a qualified pilot instructor.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"19 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":"123452713","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.2007.4295614
J. R. González, D. Pelta
The comparison of protein structures is an important problem in bioinformatics, and soft computing techniques were recently introduced for achieving a better representation and potentially, for getting better solving strategies. We focus here in the generalized maximum fuzzy contact map overlap model for analyzing the impact of the fuzzy contact map's definition, and the relation between the crisp and fuzzy costs. Surprisingly, we detected some situations where solving the fuzzy model gave better results in terms of crisp values than solving the crisp model directly.
{"title":"On Using Fuzzy Contact Maps for Protein Structure Comparison","authors":"J. R. González, D. Pelta","doi":"10.1109/FUZZY.2007.4295614","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295614","url":null,"abstract":"The comparison of protein structures is an important problem in bioinformatics, and soft computing techniques were recently introduced for achieving a better representation and potentially, for getting better solving strategies. We focus here in the generalized maximum fuzzy contact map overlap model for analyzing the impact of the fuzzy contact map's definition, and the relation between the crisp and fuzzy costs. Surprisingly, we detected some situations where solving the fuzzy model gave better results in terms of crisp values than solving the crisp model directly.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"84 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":"122642187","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.2007.4295362
A. Chatterjee, M. Dutta, A. Rakshit
A sophisticated impedance measurement technique, using an automatic digital ac bridge, is developed which is capable of providing fast and accurate real life measurement. The measurement technique employs LMS algorithm to achieve fast balance in real time. The present paper proposes to employ an intelligent neuro-fuzzy based accuracy improvement module for the LMS bridge. The objective of the neuro-fuzzy system is to add a synthetic phase offset to improve accuracy of the phase measurement in real life. The neuro-fuzzy system is successfully trained by employing particle swarm optimization (PSO), a relatively new combinatorial metaheuristic technique. The success of the proposed technique is effectively demonstrated by employing the bridge in real life for a variety of unknown impedances under measurement.
{"title":"An Intelligent Method of Impedance Measurement Employing PSO-Aided Neuro-Fuzzy System with LMS Algorithm","authors":"A. Chatterjee, M. Dutta, A. Rakshit","doi":"10.1109/FUZZY.2007.4295362","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295362","url":null,"abstract":"A sophisticated impedance measurement technique, using an automatic digital ac bridge, is developed which is capable of providing fast and accurate real life measurement. The measurement technique employs LMS algorithm to achieve fast balance in real time. The present paper proposes to employ an intelligent neuro-fuzzy based accuracy improvement module for the LMS bridge. The objective of the neuro-fuzzy system is to add a synthetic phase offset to improve accuracy of the phase measurement in real life. The neuro-fuzzy system is successfully trained by employing particle swarm optimization (PSO), a relatively new combinatorial metaheuristic technique. The success of the proposed technique is effectively demonstrated by employing the bridge in real life for a variety of unknown impedances under measurement.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"82 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":"126244205","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.2007.4295485
J. M. Alonso, O. Cordón, S. Guillaume, L. Magdalena
This work shows how to achieve a good interpretability-accuracy trade-off through keeping the strong fuzzy partition property along the whole fuzzy modeling process. First, a small compact knowledge base is built. It is highly interpretable and reasonably accurate. Second, an optimization procedure, which only affects the fuzzy partitions defining the system variables, is carried out. It improves the system accuracy while preserving the system interpretability. Two optimization strategies are compared: Solis-Wetts, a local search based strategy; and Genetic Tuning, a global search based strategy. Results obtained in a well-known benchmark medical classification problem, related to breast cancer diagnosis, show that our methodology is able to achieve knowledge bases with high interpretability and accuracy comparable to that obtained by other methodologies.
{"title":"Highly Interpretable Linguistic Knowledge Bases Optimization: Genetic Tuning versus Solis-Wetts. Looking for a good interpretability-accuracy trade-off","authors":"J. M. Alonso, O. Cordón, S. Guillaume, L. Magdalena","doi":"10.1109/FUZZY.2007.4295485","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295485","url":null,"abstract":"This work shows how to achieve a good interpretability-accuracy trade-off through keeping the strong fuzzy partition property along the whole fuzzy modeling process. First, a small compact knowledge base is built. It is highly interpretable and reasonably accurate. Second, an optimization procedure, which only affects the fuzzy partitions defining the system variables, is carried out. It improves the system accuracy while preserving the system interpretability. Two optimization strategies are compared: Solis-Wetts, a local search based strategy; and Genetic Tuning, a global search based strategy. Results obtained in a well-known benchmark medical classification problem, related to breast cancer diagnosis, show that our methodology is able to achieve knowledge bases with high interpretability and accuracy comparable to that obtained by other methodologies.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"13 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":"125765045","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.2007.4295534
Hirosato Seki, H. Ishii, M. Mizumoto
Yubazaki et al. proposed single input rule modules connected type fuzzy reasoning method (SIRMs method) whose final output is obtained by summarizing the product of the importance degree and the inference result from single input fuzzy rule module. This paper clarifies the relationship between the simplified reasoning method and SIRMs method, and shows that SIRMs method can be transformed into simplified reasoning method, but not vice versa.
{"title":"On the Property of Single Input Rule Modules Connected Type Fuzzy Reasoning Method","authors":"Hirosato Seki, H. Ishii, M. Mizumoto","doi":"10.1109/FUZZY.2007.4295534","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295534","url":null,"abstract":"Yubazaki et al. proposed single input rule modules connected type fuzzy reasoning method (SIRMs method) whose final output is obtained by summarizing the product of the importance degree and the inference result from single input fuzzy rule module. This paper clarifies the relationship between the simplified reasoning method and SIRMs method, and shows that SIRMs method can be transformed into simplified reasoning method, but not vice versa.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"1 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":"129817489","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.2007.4295480
Eduard Bartl, R. Belohlávek
The present paper deals with dependencies developed within the theory of knowledge spaces. Knowledge spaces represent a new paradigm in psychological approaches to assessment of knowledge. A distinguishing feature of knowledge spaces is their non-numerical character. The aim of the present paper is twofold. First, we bring up several remarks on data dependencies studied within knowledge spaces. Second, we consider the dependencies in a framework which is more general than that of classical knowledge spaces. Namely, we abandon the assumption that a knowledge state is a set of problems/questions which an individual is able to solve. Instead, we assume that a knowledge state is a graded set (fuzzy set) of problems. Our assumption accounts for situations where it is possible that an individual can solve a particular problem partially, rather than just "can solve" or "cannot solve". We propose a definition of dependencies and validity of dependencies in knowledge spaces with graded knowledge states, provide selected properties of the dependencies, and a lemma which serves as a bridge to existing results on so-called fuzzy attribute implications.
{"title":"Knowledge Spaces, Attribute Dependencies, and Graded Knowledge States","authors":"Eduard Bartl, R. Belohlávek","doi":"10.1109/FUZZY.2007.4295480","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295480","url":null,"abstract":"The present paper deals with dependencies developed within the theory of knowledge spaces. Knowledge spaces represent a new paradigm in psychological approaches to assessment of knowledge. A distinguishing feature of knowledge spaces is their non-numerical character. The aim of the present paper is twofold. First, we bring up several remarks on data dependencies studied within knowledge spaces. Second, we consider the dependencies in a framework which is more general than that of classical knowledge spaces. Namely, we abandon the assumption that a knowledge state is a set of problems/questions which an individual is able to solve. Instead, we assume that a knowledge state is a graded set (fuzzy set) of problems. Our assumption accounts for situations where it is possible that an individual can solve a particular problem partially, rather than just \"can solve\" or \"cannot solve\". We propose a definition of dependencies and validity of dependencies in knowledge spaces with graded knowledge states, provide selected properties of the dependencies, and a lemma which serves as a bridge to existing results on so-called fuzzy attribute implications.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"1 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":"129847806","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.2007.4295572
F. P. Romero, J. A. Olivas, P. J. Garcés
In this paper, a novel document filtering model in dynamic web repositories based on fuzzy deformable prototypes is presented. This model is based on fuzzy hierarchical categorization of documents. It defines an easy process to deal with the incoming documents and an efficient method to update their structure. The process is performed comparing the fuzzy prototypes of document cluster with the available information about documents contents. It exploits conceptual-based filtering criteria and category-based filtering techniques to deliver to the user an intelligent structure of the documents. Since filtering is a dynamic process, the knowledge base can update the hierarchy of existing documents. The clusters hierarchy can be easily and efficiently updated when new documents income on the repository by means of an inference method which is based on fuzzy deformable prototypes.
{"title":"Inference based on Fuzzy Deformable Prototypes for information filtering in dynamic web repositories","authors":"F. P. Romero, J. A. Olivas, P. J. Garcés","doi":"10.1109/FUZZY.2007.4295572","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295572","url":null,"abstract":"In this paper, a novel document filtering model in dynamic web repositories based on fuzzy deformable prototypes is presented. This model is based on fuzzy hierarchical categorization of documents. It defines an easy process to deal with the incoming documents and an efficient method to update their structure. The process is performed comparing the fuzzy prototypes of document cluster with the available information about documents contents. It exploits conceptual-based filtering criteria and category-based filtering techniques to deliver to the user an intelligent structure of the documents. Since filtering is a dynamic process, the knowledge base can update the hierarchy of existing documents. The clusters hierarchy can be easily and efficiently updated when new documents income on the repository by means of an inference method which is based on fuzzy deformable prototypes.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"69 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":"130131876","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.2007.4295423
H. Lam, C. Yeung, F. Leung
This paper presents the stability analysis and performance design for nonlinear systems. The T-S fuzzy model is employed to represent the nonlinear plant to facilitate the stability analysis. A fuzzy controller, under imperfect premise matching such that the T-S fuzzy model and the fuzzy controller do not share the same membership functions, is proposed to perform the control task. Consequently, the design flexibility can be enhanced and simple membership functions can be employed to lower the structural complexity of the fuzzy controller. However, the favourable characteristic given by perfect premise matching will vanish, which leads to conservative stability conditions. In this paper, under imperfect premise matching, the information of membership functions of the fuzzy model and controller is considered during the stability analysis. LMI-based stability conditions are derived to guarantee the system stability using the Lyapunov-based approach. Free matrices are introduced to alleviate the conservativeness of the stability conditions. LMI-based performance conditions are also derived to guarantee the system performance. Simulation examples are given to illustrate the effectiveness of the proposed approach.
{"title":"Stability Analysis and Performance Deign for Fuzzy-Model-Based Control System under Imperfect Premise Matching","authors":"H. Lam, C. Yeung, F. Leung","doi":"10.1109/FUZZY.2007.4295423","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295423","url":null,"abstract":"This paper presents the stability analysis and performance design for nonlinear systems. The T-S fuzzy model is employed to represent the nonlinear plant to facilitate the stability analysis. A fuzzy controller, under imperfect premise matching such that the T-S fuzzy model and the fuzzy controller do not share the same membership functions, is proposed to perform the control task. Consequently, the design flexibility can be enhanced and simple membership functions can be employed to lower the structural complexity of the fuzzy controller. However, the favourable characteristic given by perfect premise matching will vanish, which leads to conservative stability conditions. In this paper, under imperfect premise matching, the information of membership functions of the fuzzy model and controller is considered during the stability analysis. LMI-based stability conditions are derived to guarantee the system stability using the Lyapunov-based approach. Free matrices are introduced to alleviate the conservativeness of the stability conditions. LMI-based performance conditions are also derived to guarantee the system performance. Simulation examples are given to illustrate the effectiveness of the proposed approach.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"4 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":"129227524","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.2007.4295476
Minyar Sassi Hidri, A. Touzi, Habib Ounelli
The purpose of this paper is to construct structural information from the original data, where the results of fuzzy clustering can be displayed and interpreted. We use fuzzy formal concept analysis (FFCA) based technique for visual data mining and fuzzy clustering results interpretation. The visual interpretation and the navigation in the fuzzy lattice provided useful insights about the overlapping of different clusters and their relationships.
{"title":"Interpreting Fuzzy Clustering Results based on Fuzzy Formal Concept Analysis","authors":"Minyar Sassi Hidri, A. Touzi, Habib Ounelli","doi":"10.1109/FUZZY.2007.4295476","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295476","url":null,"abstract":"The purpose of this paper is to construct structural information from the original data, where the results of fuzzy clustering can be displayed and interpreted. We use fuzzy formal concept analysis (FFCA) based technique for visual data mining and fuzzy clustering results interpretation. The visual interpretation and the navigation in the fuzzy lattice provided useful insights about the overlapping of different clusters and their relationships.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"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":"128490350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：