Use of evolutionary computation techniques for exploration and prediction of helicopter loads

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@InProceedings{Cheung:2012:CEC,
  title =        "Use of evolutionary computation techniques for
                 exploration and prediction of helicopter loads",
  author =       "Catherine Cheung and Julio J. Valdes and Matthew Li",
  pages =        "1130--1137",
  booktitle =    "Proceedings of the 2012 IEEE Congress on Evolutionary
                 Computation",
  year =         "2012",
  editor =       "Xiaodong Li",
  month =        "10-15 " # jun,
  DOI =          "doi:10.1109/CEC.2012.6252905",
  address =      "Brisbane, Australia",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Ensemble
                 Methods in Computational Intelligence (IEEE-CEC),
                 Defence and cyber security, Classification, clustering,
                 data analysis and data mining",
  abstract =     "The development of accurate load spectra for
                 helicopters is necessary for life cycle management and
                 life extension efforts. This paper explores continued
                 efforts to use evolutionary computation (EC) methods
                 and machine learning techniques to estimate several
                 helicopter dynamic loads. Estimates for the main rotor
                 normal bending (MRNBX) on the Australian Black Hawk
                 helicopter were generated from an input set that
                 included thirty standard flight state and control
                 system parameters under several flight conditions (full
                 speed forward level flight, rolling left pullout at
                 1.5g, and steady 45deg left turn at full speed).
                 Multiobjective genetic algorithms (MOGA) used in
                 combination with the Gamma test found reduced subsets
                 of predictor variables with Madelin potential. These
                 subsets were used to estimate MRNBX using Cartesian
                 genetic programming and neural network models trained
                 by deterministic and evolutionary computation
                 techniques, including particle swarm optimization
                 (PSO), differential evolution (DE), and MOGA. PSO and
                 DE were used alone or in combination with deterministic
                 methods. Different error measures were explored
                 including a fuzzy-based asymmetric error function. EC
                 techniques played an important role in both the
                 exploratory and Madelin phase of the investigation. The
                 results of this work show that the addition of EC
                 techniques in the modelling stage generated more
                 accurate and correlated models than could be obtained
                 using only deterministic optimization.",
  notes =        "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
                 EPS and the IET.",
}

Genetic Programming entries for Catherine Cheung Julio J Valdes Matthew Li

Citations