2008 3rd International Workshop on Genetic and Evolving Systems最新文献_第2页

Evolving fuzzy inferential sensors for process industry 过程工业模糊推理传感器的进化

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484565

P. Angelov, A. Kordon, Xiaowei Zhou

{"title":"Evolving fuzzy inferential sensors for process industry","authors":"P. Angelov, A. Kordon, Xiaowei Zhou","doi":"10.1109/GEFS.2008.4484565","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484565","url":null,"abstract":"This paper describes an approach to design self-developing and self-tuning inferential soft sensors applicable to process industries. The proposal is for a Takagi-Sugeno-fuzzy system framework that has evolving (open structure) architecture, and an on-line (possibly real-time) learning algorithm. The proposed methodology is novel and it addresses the problems of self-development and self-calibration caused by drift in the data patterns due to changes in the operating regimes, catalysts ageing, industrial equipment wearing, contamination etc. The proposed computational technique is data-driven and parameter-free (it only requires a couple of parameters with clear meaning and suggested values). In this paper a case study of four problems of estimation of chemical properties is considered, however, the methodology has a much wider validity. The optimal inputs to the proposed evolving inferential sensor are determined a priori and off-line using a multi-objective genetic-programming-based optimization. Different on-line input selection techniques are under development. The methodology is validated on real data provided by the Dow Chemical Company, USA.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"128 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":"117070206","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: 12

Processing times estimation in a manufacturing industry through genetic programming 基于遗传规划的制造业加工时间估计

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484574

M. Mucientes, J. Vidal, Alberto Bugarín-Diz, M. Lama

{"title":"Processing times estimation in a manufacturing industry through genetic programming","authors":"M. Mucientes, J. Vidal, Alberto Bugarín-Diz, M. Lama","doi":"10.1109/GEFS.2008.4484574","DOIUrl":"https://doi.org/10.1109/GEFS.2008.4484574","url":null,"abstract":"Accuracy in processing time estimation of manufacturing operations is fundamental to achieve more competitive prices and higher profits in an industry. The manufacturing times of a machine depend on several input variables and, for each class or type of product, a regression function for that machine can be defined. Time estimations are used for implementing production plans. These plans are usually supervised and modified by an expert, so information about the dependencies of processing time with the input variables is also very important. Taking into account both premises (accuracy and simplicity in information extraction), a model based on TSK (Takagi-Sugeno-Kang) fuzzy rules has been used. TSK rules fulfill both requisites: the system has a high accuracy, and the knowledge structure makes explicit the dependencies between time estimations and the input variables. We propose a TSK fuzzy rule model in which the rules have a variable structure in the consequent, as the regression functions can be completely distinct for different machines or, even, for different classes of inputs to the same machine. The methodology to learn the TSK knowledge base is based on genetic programming together with a context-free grammar to restrict the valid structures of the regression functions. The system has been tested with real data coming from five different machines of a wood furniture industry.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"27 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":"114507648","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: 9

A novel genetic cooperative-competitive fuzzy rule based learning method using genetic programming for high dimensional problems 基于遗传规划的高维问题遗传合作-竞争模糊规则学习方法

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484575

F. Berlanga, M. J. Jesús, F. Herrera

引用次数: 6

A first study on bagging fuzzy rule-based classification systems with multicriteria genetic selection of the component classifiers 基于bagging模糊规则的多准则分类器遗传选择研究

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484560

O. Cordón, A. Quirin, L. Sánchez

引用次数: 14

Evolutionary generation of rule base in TSK fuzzy model for real estate appraisal 房地产估价TSK模糊模型规则库的演化生成

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484570

T. Lasota, B. Trawinski, Krzysztof Trawiński

引用次数: 7

KEEL: A data mining software tool integrating genetic fuzzy systems 一个集成遗传模糊系统的数据挖掘软件工具

2008 3rd International Workshop on Genetic and Evolving Systems Pub Date : 2008-03-04 DOI: 10.1109/GEFS.2008.4484572

J. Alcalá-Fdez, S. García, F. Berlanga, Alberto Fernández, L. Sánchez, M. J. Jesús, F. Herrera

引用次数: 15