2008 3rd International Workshop on Genetic and Evolving Systems最新文献

Designing fuzzy rule-based classifiers that can visually explain their classification results to human users 设计基于模糊规则的分类器，可以直观地向人类用户解释其分类结果

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484559

H. Ishibuchi, Y. Kaisho, Y. Nojima

{"title":"Designing fuzzy rule-based classifiers that can visually explain their classification results to human users","authors":"H. Ishibuchi, Y. Kaisho, Y. Nojima","doi":"10.1109/GEFS.2008.4484559","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484559","url":null,"abstract":"In various application areas of fuzzy rule-based systems, human users want to know why a particular reasoning result is obtained. That is, fuzzy rule-based systems are required to have high explanation ability. In this paper, we propose an approach to the design of fuzzy rule-based classifiers that can visually explain their classification results to human users. That is, our fuzzy rule-based classifiers can explain to human users why an input pattern is classified as a particular class in an understandable manner. The proposed approach consists of a rule selection method and a visualization interface. Our idea is to design fuzzy rule-based classifiers using fuzzy rules with only two antecedent conditions. A genetic algorithm is employed to construct a compact fuzzy rule-based classifier by choosing only a small number of fuzzy rules. In the classification phase, we use a single winner rule-based method for classifying an input pattern. The classification result of the input pattern is visually explained in a two-dimensional space where the two antecedent conditions of the winner rule are defined. Our approach is compared with feature selection by computational experiments.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124999910","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}

引用次数: 17

Applying evolving fuzzy models with adaptive local error bars to on-line fault detection 将带有自适应局部误差条的演化模糊模型应用于在线故障检测

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484564

E. Lughofer, C. Guardiola

{"title":"Applying evolving fuzzy models with adaptive local error bars to on-line fault detection","authors":"E. Lughofer, C. Guardiola","doi":"10.1109/GEFS.2008.4484564","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484564","url":null,"abstract":"The main contribution of this paper is a novel fault detection strategy, which is able to cope with changing system states at on-line measurement systems fully automatically. For doing so, an improved fault detection logic is introduced which is based on data-driven evolving fuzzy models. These are sample-wise trained from online measurement data, i.e. the structure and rules of the models evolve over time in order to cope 1.) with high-frequented measurement recordings and 2.) online changing operating conditions. The evolving fuzzy models represent (changing) non-linear dependencies between certain system variables and are used for calculating the deviation between expected model outputs and real measured values on new incoming data samples (rarr residuals). The residuals are compared with local confidence regions surrounding the evolving fuzzy models, so-called local error bars, incrementally calculated synchronously to the models. The behavior of the residuals is analyzed over time by an adaptive univariate statistical approach. Evaluation results based on high-dimensional measurement data from engine test benches are demonstrated at the end of the paper, where the novel fault detection approach is compared against static analytical (fault) models.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121540983","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}

引用次数: 20

Instantaneous anomaly detection in online learning fuzzy systems 在线学习模糊系统中的瞬时异常检测

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484562

W. Brockmann, N. Rosemann

引用次数: 6

Toward evolving consistent, complete, and compact fuzzy rule sets for classification problems 演化出一致、完整、紧凑的模糊规则集用于分类问题

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484573

J. Casillas, A. Orriols-Puig, Ester Bernadó-Mansilla

引用次数: 9

Evolving fuzzy classifier system using PSO for RoboCup vision applications 基于粒子群算法的模糊分类器系统在机器人世界杯视觉中的应用

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484561

N. S. Milani, Alireza Kashanipour, A. R. Kashanipour

{"title":"Evolving fuzzy classifier system using PSO for RoboCup vision applications","authors":"N. S. Milani, Alireza Kashanipour, A. R. Kashanipour","doi":"10.1109/GEFS.2008.4484561","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484561","url":null,"abstract":"In this paper we propose a color classification algorithm in which an evolutionary design optimizes a fuzzy system for color classification and image segmentation. This system works with the least number of rules and has minimum error rate by the mean of particle swarm optimization (PSO) method. In this approach each particle of the swarm codes a set of fuzzy rules. During evolution, each member of a population tries to maximize a fitness criterion which has designed to raise classification rate and to reduce the number of rules. Finally, the particle with the highest fitness value is selected as the best set of fuzzy rules for image segmentation. Fuzzy sets are defined on the H, S and L components of the HSL Color Space to provide a fuzzy logic model which aims to follow the human intuition of Color Classification. Color-based vision applications face the challenge of color variations by illumination. The final system designed by this method is adaptive to continuous variable lighting according to its evolving-fuzzy nature. In this method parameters setting is done only once .The experimental results in RoboCup leagues demonstrate that the presented approach can be very robust to noise and light variations.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133607982","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}

引用次数: 1

Towards a fuzzy evaluation of the adaptivity degree of an evolving agent 进化主体自适应程度的模糊评价

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484563

I. Kallel, S. Mezghani, A. Alimi

引用次数: 10

Driving condition recognition for genetic-fuzzy HEV Control 遗传模糊混合动力汽车控制的驾驶状态识别

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484569

M. Montazeri-Gh, A. Ahmadi, M. Asadi

引用次数: 38

Coevolutionary fuzzy multiagent bidding strategies in competitive electricity markets 竞争电力市场中的协同进化模糊多智能体竞价策略

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484567

I. Walter, F. Gomide

{"title":"Coevolutionary fuzzy multiagent bidding strategies in competitive electricity markets","authors":"I. Walter, F. Gomide","doi":"10.1109/GEFS.2008.4484567","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484567","url":null,"abstract":"Following the development of online markets, trading practices as dynamic pricing, online auctions and exchanges have become relevant to a variety of markets. In this paper we suggest a machine learning approach to find a suitable bidding strategy for an auction participant using information commonly available in online auction settings. We take the electricity auction as the main application example, due to its importance as an experimental instance of the suggested approach. In previous works we evolved successful fuzzy bidding strategies. Here we introduce a coevolutionary algorithm to study how the evolving strategies react to each other in a more dynamic environment. By enabling a fuzzy system to learn trough an evolutionary algorithm one expects to find effective and transparent bidding strategies. By adopting a coevolutionary approach a more realistic representation of the agents participating in an auction based electricity market allows the evolutionary bidding strategies interact. The results show that the coevolutionary approach can improve agents profits at the cost of increasing system hourly price paid by demand.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132964346","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}

引用次数: 5

Evolutionary identification of a recurrent fuzzy neural network with enhanced memory capabilities 具有增强记忆能力的递归模糊神经网络的进化识别

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484571

D. Stavrakoudis, A. K. Papastamoulis, Ioannis B. Theocharis

引用次数: 0

Tuning a fuzzy racing car by coevolution 通过共同进化调整一辆模糊赛车

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484568

S. Guadarrama, Ruben Vazquez

引用次数: 9