Symbolic Regression for Marine Vehicles Identification

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@Article{MorenoSalinas:2015:IFAC-PapersOnLine,
  author =       "D. Moreno-Salinas and E. Besada-Portas and 
                 J. A. Lopez-Orozco and D. Chaos and J. M. {de la Cruz} and 
                 J. Aranda",
  title =        "Symbolic Regression for Marine Vehicles
                 Identification",
  journal =      "IFAC-PapersOnLine",
  volume =       "48",
  number =       "16",
  pages =        "210--216",
  year =         "2015",
  note =         "10th IFAC Conference on Manoeuvring and Control of
                 Marine Craft MCMC 2015, Copenhagen, 24-26 August 2015",
  ISSN =         "2405-8963",
  DOI =          "doi:10.1016/j.ifacol.2015.10.282",
  URL =          "http://www.sciencedirect.com/science/article/pii/S2405896315021734",
  abstract =     "The mathematical models used in simulation must be
                 reliable and trustworthy enough to describe the real
                 systems with an appropriate accuracy. This simulation
                 process is specially important in marine environment
                 due to the changing environmental conditions, to the
                 cost of the infrastructure needed to carry out tests,
                 and to the need of calibration, deployment and recovery
                 of the marine systems. If a reliable mathematical model
                 of the vehicle is available, a part of the experimental
                 tests can be avoided. In this paper we present a system
                 identification technique based on genetic programming,
                 the symbolic regression, to be applied on marine
                 systems. In this sense, we show that it is possible to
                 obtain a mathematical model of a ship for control
                 purposes without the need of describing or knowing the
                 model structure in advance, i.e., the identification
                 itself provides the model structure that better
                 describes the system. Thus, we can define a reliable
                 black-box model that is computed in a simple way and
                 where no many experimental data are needed. The model
                 obtained is tested with additional data and manoeuvres
                 to show its good performance and prediction ability.",
  keywords =     "genetic algorithms, genetic programming, Autonomous
                 vehicles, marine systems, identification, symbolic
                 regression",
}

Genetic Programming entries for D Moreno-Salinas E Besada-Portas J A Lopez-Orozco D Chaos Jesus Manuel de la Cruz J Aranda

Citations