A comparison of techniques to get sparse rational approximations for linear fractional representations

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

@InProceedings{Roos:2014:ICAS,
  title =        "A comparison of techniques to get sparse rational
                 approximations for linear fractional representations",
  author =       "Clement Roos and Georges Hardier and Carsten Doell",
  booktitle =    "29th Congress of the International Council of the
                 Aeronautical Sciences (ICAS 2014)",
  year =         "2014",
  address =      "Saint-Petersburg, Russia",
  month =        "7-12 " # sep,
  organisation = "International Council of the Aeronautical Sciences
                 (ICAS)",
  publisher =    "HAL CCSD",
  keywords =     "genetic algorithms, genetic programming, POLYNOMIAL
                 AND RATIONAL APPROXIMATION, LINEAR FRACTIONAL
                 REPRESENTATION, SURROGATE MODELS, EVOLUTIONARY
                 ALGORITHMS",
  ISSN =         "01088599",
  bibsource =    "OAI-PMH server at api.archives-ouvertes.fr",
  contributor =  "Onera - The French Aerospace Lab (Toulouse) and
                 ONERA",
  coverage =     "Saint-Petersburg, Russia",
  description =  "International audience",
  identifier =   "hal-01088599",
  language =     "en",
  oai =          "oai:HAL:hal-01088599v1",
  URL =          "https://hal.archives-ouvertes.fr/hal-01088599",
  URL =          "https://hal.archives-ouvertes.fr/hal-01088599/document",
  size =         "12 pages",
  abstract =     "The objective of this paper is to stress that the size
                 of a Linear Fractional Representation (LFR)
                 significantly depends on the way tabulated or
                 irrational data are approximated during the prior
                 modelling process. It is notably shown that rational
                 approximants can result in much smaller LFR than
                 polynomial ones. Accordingly, 2 new methods are
                 proposed to generate sparse rational models, which
                 avoid data over fitting and lead to simple yet accurate
                 LFR. The 1 st one builds a parsimonious modelling based
                 on surrogate models and a new powerful global
                 optimisation method, and then translates the result
                 into a fractional form. The 2 nd one looks for a
                 rational approximant in a single step thanks to a
                 symbolic regression technique, and relies on Genetic
                 Programming to select sparse monomials. This work takes
                 place in a more general project led by ONERA/DCSD and
                 aimed at developing a Systems Modelling, Analysis and
                 Control Toolbox (SMAC) for Matlab.",
  notes =        "Toulouse - Onera - The French Aerospace Lab
                 http://www.icas2014.com/",
}

Genetic Programming entries for Clement Roos Georges Hardier Carsten Doell

Citations