Genetic programming approach to predict torque and brake specific fuel consumption of a gasoline engine
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Togun20103401,
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author = "Necla Togun and Sedat Baysec",
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title = "Genetic programming approach to predict torque and
brake specific fuel consumption of a gasoline engine",
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journal = "Applied Energy",
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volume = "87",
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number = "11",
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pages = "3401--3408",
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year = "2010",
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ISSN = "0306-2619",
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DOI = "doi:10.1016/j.apenergy.2010.04.027",
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URL = "http://www.sciencedirect.com/science/article/B6V1T-506P5PR-2/2/3ce08e476cfb1819b6e03a4571cad2cd",
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keywords = "genetic algorithms, genetic programming, Gasoline
engine, Torque, Brake specific fuel consumption,
Explicit solution, Modelling engine",
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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
Citations