Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277068
Hua Ke, Weimin Ma
In real projects, both the trade-off between the project cost and the project completion time, and the uncertainty of the environment are considerable aspects for decision-makers. However, the research on the time-cost tradeoff problem seldom concerns fuzzy environments. In this paper, a new fuzzy time-cost trade-off model with the philosophy of dependent-chance programming is proposed, in which credibility theory is applied to describe the uncertainty of activity durations. A searching method as a hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is produced to search the optimal schedule under the given decision-making rule. The purpose of the paper is to reveal how to obtain the optimal balance of the project completion time and the project cost in fuzzy environment.
{"title":"A dependent-chance programming model for fuzzy time-cost trade-off problem","authors":"Hua Ke, Weimin Ma","doi":"10.1109/FUZZY.2009.5277068","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277068","url":null,"abstract":"In real projects, both the trade-off between the project cost and the project completion time, and the uncertainty of the environment are considerable aspects for decision-makers. However, the research on the time-cost tradeoff problem seldom concerns fuzzy environments. In this paper, a new fuzzy time-cost trade-off model with the philosophy of dependent-chance programming is proposed, in which credibility theory is applied to describe the uncertainty of activity durations. A searching method as a hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is produced to search the optimal schedule under the given decision-making rule. The purpose of the paper is to reveal how to obtain the optimal balance of the project completion time and the project cost in fuzzy environment.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133756718","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277334
A. Notsu, Katsuhiro Honda, H. Ichihashi
A Conceptual Graph Generation method is proposed in this paper. A Conceptual Graph is useful for studying human verbal caring interactions such as counseling, based on an interpersonal psychological approach referred to as ‘Naïve Psychology’. We apply the Visual Assessment of Clustering Tendency (VAT) to naïve psychology, with particular reference to the visual understanding of people. A Conceptual Graph is constructed from words and sentences selected by morphological analysis. Furthermore, the VAT algorithm produces a visual display that can be used to assess clustering tendencies in a set of persons (notions) by reconstructing a digital image representation of a square relational dissimilarity matrix. This algorithm clearly represents two types of imbalanced situations in naïve psychology: namely the crisp and fuzzy situations. In addition, social simulations that utilize several graphs are introduced.
{"title":"Conceptual graph generation from text documents based on perceptual balance","authors":"A. Notsu, Katsuhiro Honda, H. Ichihashi","doi":"10.1109/FUZZY.2009.5277334","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277334","url":null,"abstract":"A Conceptual Graph Generation method is proposed in this paper. A Conceptual Graph is useful for studying human verbal caring interactions such as counseling, based on an interpersonal psychological approach referred to as ‘Naïve Psychology’. We apply the Visual Assessment of Clustering Tendency (VAT) to naïve psychology, with particular reference to the visual understanding of people. A Conceptual Graph is constructed from words and sentences selected by morphological analysis. Furthermore, the VAT algorithm produces a visual display that can be used to assess clustering tendencies in a set of persons (notions) by reconstructing a digital image representation of a square relational dissimilarity matrix. This algorithm clearly represents two types of imbalanced situations in naïve psychology: namely the crisp and fuzzy situations. In addition, social simulations that utilize several graphs are introduced.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122212511","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277269
Tai-Ning Yang, Chih-Jen Lee, Shi-Jim Yen
In this paper, we consider the issue of fuzzy objective functions when outliers exist. The outlier set is defined as the complement of the data set. Following this concept, a specially designed fuzzy membership weighted objective function is proposed and the corresponding optimal membership is derived. Based on the proposed robust objective functions, algorithms for clustering are implemented. Artificially generated data are used for comparison.
{"title":"Fuzzy objective functions for robust pattern recognition","authors":"Tai-Ning Yang, Chih-Jen Lee, Shi-Jim Yen","doi":"10.1109/FUZZY.2009.5277269","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277269","url":null,"abstract":"In this paper, we consider the issue of fuzzy objective functions when outliers exist. The outlier set is defined as the complement of the data set. Following this concept, a specially designed fuzzy membership weighted objective function is proposed and the corresponding optimal membership is derived. Based on the proposed robust objective functions, algorithms for clustering are implemented. Artificially generated data are used for comparison.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125747646","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277245
Young Cho
We will discuss about the intelligent multi-agent based convergence system in AI. The multi-agent concept is varied from 2 or 3 agents to many agents. Therefore, to construct the appropriate concept which you want to implement is more important. In this paper, we will discuss the concept of multi-agent, and discuss some application areas of fuzzy logic based multi-agent, especially in bioinformatics and digital library etc. And finally we will discuss about the future research areas of multi-agent in AI.
{"title":"Intelligent multi-agent based convergence systems","authors":"Young Cho","doi":"10.1109/FUZZY.2009.5277245","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277245","url":null,"abstract":"We will discuss about the intelligent multi-agent based convergence system in AI. The multi-agent concept is varied from 2 or 3 agents to many agents. Therefore, to construct the appropriate concept which you want to implement is more important. In this paper, we will discuss the concept of multi-agent, and discuss some application areas of fuzzy logic based multi-agent, especially in bioinformatics and digital library etc. And finally we will discuss about the future research areas of multi-agent in AI.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125670604","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277163
H. Sung, Jin Bae Park, Jong-Seon Kim, Y. Joo
In this paper, we concern an intelligent digital re-design(IDR) method for a fuzzy observer-based output-feedback control system which includes parametric uncertainties. The term IDR is to convert an existing analog control into an equivalent digital counterpart via state-matching. The considered IDR problem is viewed as convex minimization problem of the norm distances between linear operators to be matched and its constructive condition is formulated in terms of linear matrix inequalities (LMIs). The main features of the proposed method are that the state estimation error in the plant dynamics is considered in the IDR condition that plays a crucial role in the performance improvement; the uncertainties in the plant dynamics is shown in the IDR condition by virtue of the bilinear and inverse-bilinear approximation method; finally, the stability property is preserved by the proposed IDR method.
{"title":"Output-feedback sampled-data control for uncertain nonlinear system","authors":"H. Sung, Jin Bae Park, Jong-Seon Kim, Y. Joo","doi":"10.1109/FUZZY.2009.5277163","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277163","url":null,"abstract":"In this paper, we concern an intelligent digital re-design(IDR) method for a fuzzy observer-based output-feedback control system which includes parametric uncertainties. The term IDR is to convert an existing analog control into an equivalent digital counterpart via state-matching. The considered IDR problem is viewed as convex minimization problem of the norm distances between linear operators to be matched and its constructive condition is formulated in terms of linear matrix inequalities (LMIs). The main features of the proposed method are that the state estimation error in the plant dynamics is considered in the IDR condition that plays a crucial role in the performance improvement; the uncertainties in the plant dynamics is shown in the IDR condition by virtue of the bilinear and inverse-bilinear approximation method; finally, the stability property is preserved by the proposed IDR method.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127163410","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277300
Alexander Fölling, C. Grimme, Joachim Lepping, A. Papaspyrou
In our work, we utilize a competitive Co-evolutionary Algorithm in order to optimize the parameter set of a Fuzzy System for job exchange in Computational Grids. In this domain, the providers of High Performance Computing (HPC) centers strive for minimizing the response time for their own customers by trying to distribute workload to other sites in the Grid environment. The Fuzzy System is used for steering each site's decisions whether to distribute or accept workload in a beneficial, yet egoistic direction. This scenario is particularly suited for the application of a competitive CA: Grid sites' Fuzzy Systems are modeled as species, which evolve in different populations. While each species tries to minimize the response time for locally submitted jobs, their individuals' fitness is determined within the commonly shared ecosystem. Using real workload traces and Grid setups, we show that the opportunistic cooperation leads to significant improvements for both each Grid site and the overall system.
{"title":"Co-evolving fuzzy rule sets for job exchange in computational grids","authors":"Alexander Fölling, C. Grimme, Joachim Lepping, A. Papaspyrou","doi":"10.1109/FUZZY.2009.5277300","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277300","url":null,"abstract":"In our work, we utilize a competitive Co-evolutionary Algorithm in order to optimize the parameter set of a Fuzzy System for job exchange in Computational Grids. In this domain, the providers of High Performance Computing (HPC) centers strive for minimizing the response time for their own customers by trying to distribute workload to other sites in the Grid environment. The Fuzzy System is used for steering each site's decisions whether to distribute or accept workload in a beneficial, yet egoistic direction. This scenario is particularly suited for the application of a competitive CA: Grid sites' Fuzzy Systems are modeled as species, which evolve in different populations. While each species tries to minimize the response time for locally submitted jobs, their individuals' fitness is determined within the commonly shared ecosystem. Using real workload traces and Grid setups, we show that the opportunistic cooperation leads to significant improvements for both each Grid site and the overall system.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126378272","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277324
H. Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Y. Nojima
A large number of non-dominated solutions are often obtained by a single run of an evolutionary multiobjective optimization (EMO) algorithm. In the EMO research area, it is usually assumed that a single solution is to be chosen from the obtained non-dominated solutions by the decision maker. It is, however, time-consuming and not easy for the decision maker to examine a large number of obtained non-dominated solutions. Motivated by these discussions, we proposed single-objective and multiobjective formulations of solution selection problems to present only a small number of representative non-dominated solutions to the decision maker in our former study. The basic idea is to minimize the number of solutions to be presented while maximizing their hypervolume. A number of single-objective formulations can be derived from such a two-objective solution selection problem. In this paper, single-objective rule selection is performed as a post-processing procedure of EMO algorithms to select a prespecified number of non-dominated solutions (e.g., 10 or 20 solutions). Through computational experiments on multiobjective 0/1 knapsack problems, we examine the characteristic features of selected non-dominated solutions. We also examine the effect of the choice of a reference point for hypervolume calculation on the distribution of selected non-dominated solutions.
{"title":"Selecting a small number of representative non-dominated solutions by a hypervolume-based solution selection approach","authors":"H. Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Y. Nojima","doi":"10.1109/FUZZY.2009.5277324","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277324","url":null,"abstract":"A large number of non-dominated solutions are often obtained by a single run of an evolutionary multiobjective optimization (EMO) algorithm. In the EMO research area, it is usually assumed that a single solution is to be chosen from the obtained non-dominated solutions by the decision maker. It is, however, time-consuming and not easy for the decision maker to examine a large number of obtained non-dominated solutions. Motivated by these discussions, we proposed single-objective and multiobjective formulations of solution selection problems to present only a small number of representative non-dominated solutions to the decision maker in our former study. The basic idea is to minimize the number of solutions to be presented while maximizing their hypervolume. A number of single-objective formulations can be derived from such a two-objective solution selection problem. In this paper, single-objective rule selection is performed as a post-processing procedure of EMO algorithms to select a prespecified number of non-dominated solutions (e.g., 10 or 20 solutions). Through computational experiments on multiobjective 0/1 knapsack problems, we examine the characteristic features of selected non-dominated solutions. We also examine the effect of the choice of a reference point for hypervolume calculation on the distribution of selected non-dominated solutions.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117065115","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277321
Phil Birkin, J. Garibaldi
In this paper we compare the differences between type-1 and interval type-2 fuzzy logic controllers, with seven, five and two three term membership functions. The controllers were used to control a DC motor model in a closed loop simulation. The performance of each controller to a step change and a change in motor inertia with and without added noise was recorded. The results showed that there was no statistical difference between the type-1 and type-2 controllers. It was also found that a type-1 three term controller was as good as a type-1 or type-2 seven term controller, in controlling the micro robot DC motor model.
{"title":"A comparison of Type-1 and Type-2 fuzzy controllers in a micro-robot context","authors":"Phil Birkin, J. Garibaldi","doi":"10.1109/FUZZY.2009.5277321","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277321","url":null,"abstract":"In this paper we compare the differences between type-1 and interval type-2 fuzzy logic controllers, with seven, five and two three term membership functions. The controllers were used to control a DC motor model in a closed loop simulation. The performance of each controller to a step change and a change in motor inertia with and without added noise was recorded. The results showed that there was no statistical difference between the type-1 and type-2 controllers. It was also found that a type-1 three term controller was as good as a type-1 or type-2 seven term controller, in controlling the micro robot DC motor model.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121470439","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277254
E. Papageorgiou, Nikolaos I. Papandrianos, G. Karagianni, G. Kyriazopoulos, D. Sfyras
The prediction of pulmonary infections in intensive care unit is a complex medical task where a large number of parameters, tests, clinical symptoms and laboratory results are present. The knowledge of physicians according to the physical examination and clinical measurements are the main point to succeed a diagnosis and monitoring patient status. This paper presents the results of our investigation of the problem of representing knowledge for medical diagnosis systems concentrated on the pulmonary infections. The main topic of the presented effort is the representation of the cause-effect relationships within medical data by the application of the soft computing technique of fuzzy cognitive maps. The fuzzy cognitive map is a knowledge based technique for modeling and representing experts' knowledge. It can handle efficiently with complex modeling problems to assess medical decision making tasks. Due to its easy graphical representation the proposed FCM can be used to make the medical knowledge widely available through computer consultation systems.
{"title":"A fuzzy cognitive map based tool for prediction of infectious diseases","authors":"E. Papageorgiou, Nikolaos I. Papandrianos, G. Karagianni, G. Kyriazopoulos, D. Sfyras","doi":"10.1109/FUZZY.2009.5277254","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277254","url":null,"abstract":"The prediction of pulmonary infections in intensive care unit is a complex medical task where a large number of parameters, tests, clinical symptoms and laboratory results are present. The knowledge of physicians according to the physical examination and clinical measurements are the main point to succeed a diagnosis and monitoring patient status. This paper presents the results of our investigation of the problem of representing knowledge for medical diagnosis systems concentrated on the pulmonary infections. The main topic of the presented effort is the representation of the cause-effect relationships within medical data by the application of the soft computing technique of fuzzy cognitive maps. The fuzzy cognitive map is a knowledge based technique for modeling and representing experts' knowledge. It can handle efficiently with complex modeling problems to assess medical decision making tasks. Due to its easy graphical representation the proposed FCM can be used to make the medical knowledge widely available through computer consultation systems.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115462444","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277058
Richard Jensen, C. Cornelis, Q. Shen
The automated generation of feature pattern-based if-then rules is essential to the success of many intelligent pattern classifiers, especially when their inference results are expected to be directly human-comprehensible. Fuzzy and rough set theory have been applied with much success to this area as well as to feature selection. Since both applications of rough set theory involve the processing of equivalence classes for their successful operation, it is natural to combine them into a single integrated method that generates concise, meaningful and accurate rules. This paper proposes such an approach, based on fuzzy-rough sets. The algorithm is experimentally evaluated against leading classifiers, including fuzzy and rough rule inducers, and shown to be effective.
{"title":"Hybrid fuzzy-rough rule induction and feature selection","authors":"Richard Jensen, C. Cornelis, Q. Shen","doi":"10.1109/FUZZY.2009.5277058","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277058","url":null,"abstract":"The automated generation of feature pattern-based if-then rules is essential to the success of many intelligent pattern classifiers, especially when their inference results are expected to be directly human-comprehensible. Fuzzy and rough set theory have been applied with much success to this area as well as to feature selection. Since both applications of rough set theory involve the processing of equivalence classes for their successful operation, it is natural to combine them into a single integrated method that generates concise, meaningful and accurate rules. This paper proposes such an approach, based on fuzzy-rough sets. The algorithm is experimentally evaluated against leading classifiers, including fuzzy and rough rule inducers, and shown to be effective.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131084939","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：