Online modelling based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{oai:inderscience.com:12487,

• title = "Online modelling based on Genetic Programming",
• author = "Stephan Winkler and Hajrudin Efendic and Luigi {Del Re} and Michael Affenzeller and Stefan Wagner",
• journal = "International Journal of Intelligent Systems Technologies and Applications",
• year = "2007",
• volume = "2",
• number = "2/3",
• pages = "255--270",
• month = "19 " # feb,
• publisher = "Inderscience Publishers",
• ISSN = "1740-8873",
• bibsource = "OAI-PMH server at www.inderscience.com",
• language = "eng",
• oai = "oai:inderscience.com:12487",
• relation = "ISSN online: 1740-8873 ISSN print: 1740-8865 DOI: 10.1504/07.12487",
• rights = "Inderscience Copyright",
• source = "IJISTA (2007), Vol 2 Issue 2/3, pp 255 - 270",
• keywords = "genetic algorithms, genetic programming, GP, data driven model identification, self-adaption, machine learning, online modelling, fault diagnosis, automatic learning, real time",
• URL = "http://www.inderscience.com/link.php?id=12487",
• DOI = "doi:10.1504/IJISTA.2007.012487",
• abstract = "Genetic Programming (GP), a heuristic optimisation technique based on the theory of Genetic Algorithms (GAs), is a method successfully used to identify non-linear model structures by analysing a system's measured signals. Mostly, it is used as an offline tool that means that structural analysis is done after collecting all available identification data. In this paper, we propose an enhanced on-line GP approach that is able to adapt its behaviour to new observations while the GP process is executed. Furthermore, an approach using GP for online Fault Diagnosis (FD) is described, and finally test results using measurement data of NOx (nitrogen oxide, nitrogen dioxide) emissions of a BMW diesel engine are discussed.",

}

Genetic Programming entries for Stephan M Winkler Hajrudin Efendic Luigi del Re Michael Affenzeller Stefan Wagner

Citations