Genetic programming approach to predict torque and brake specific fuel consumption of a gasoline engine

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@Article{Togun20103401,
  author =       "Necla Togun and Sedat Baysec",
  title =        "Genetic programming approach to predict torque and
                 brake specific fuel consumption of a gasoline engine",
  journal =      "Applied Energy",
  volume =       "87",
  number =       "11",
  pages =        "3401--3408",
  year =         "2010",
  ISSN =         "0306-2619",
  DOI =          "doi:10.1016/j.apenergy.2010.04.027",
  URL =          "http://www.sciencedirect.com/science/article/B6V1T-506P5PR-2/2/3ce08e476cfb1819b6e03a4571cad2cd",
  keywords =     "genetic algorithms, genetic programming, Gasoline
                 engine, Torque, Brake specific fuel consumption,
                 Explicit solution, Modelling engine",
  abstract =     "This study presents genetic programming (GP) based
                 model to predict the torque and brake specific fuel
                 consumption a gasoline engine in terms of spark
                 advance, throttle position and engine speed. The
                 objective of this study is to develop an alternative
                 robust formulations based on experimental data and to
                 verify the use of GP for generating the formulations
                 for gasoline engine torque and brake specific fuel
                 consumption. Experimental studies were completed to
                 obtain training and testing data. Of all 81 data sets,
                 the training and testing sets consisted of randomly
                 selected 63 and 18 sets, respectively. Considerable
                 good performance was achieved in predicting gasoline
                 engine torque and brake specific fuel consumption by
                 using GP. The performance of accuracies of proposed GP
                 models are quite satisfactory (R2 = 0.9878 for gasoline
                 engine torque and R2 = 0.9744 for gasoline engine brake
                 specific fuel consumption). The prediction of proposed
                 GP models were compared to those of the neural network
                 modeling, and strictly good agreement was observed
                 between the two predictions. The proposed GP
                 formulation is quite accurate, fast and practical.",
}

Genetic Programming entries for Necla Kara Togun Sedat Baysec

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