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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