Open channel junction velocity prediction by using a hybrid self-neuron adjustable artificial neural network
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Bonakdari:2016:FMI,
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author = "Hossein Bonakdari and Amir Hossein Zaji",
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title = "Open channel junction velocity prediction by using a
hybrid self-neuron adjustable artificial neural
network",
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journal = "Flow Measurement and Instrumentation",
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volume = "49",
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pages = "46--51",
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year = "2016",
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ISSN = "0955-5986",
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DOI = "doi:10.1016/j.flowmeasinst.2016.04.003",
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URL = "http://www.sciencedirect.com/science/article/pii/S0955598616300309",
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abstract = "Determining the appropriate hidden layers neuron
number is one of the most important processes in
modelling the Multi-Layer Perceptron Artificial Neural
Network (MLP-ANN). Despite the significant effect of
the MLP-ANN neurons number on predicting accuracy,
there is no definite rule for its determination. In
this study, a new self-neuron number adjustable, hybrid
Genetic Algorithm-Artificial Neural Network (GA-ANN),
is introduced and its application examined on the
complex velocity field prediction of an open channel
junction. The results of GA-ANN were compared with
those got by the Genetic Programming (GP) method as two
applications of the Genetic Algorithm (GA). The
comparisons showed that the GA-ANN model can predict
the open channel junction velocity with higher accuracy
than the GP model, with Root Mean Squared Error (RMSE)
of 0.086 and 0.156, respectively. Finally the equation,
obtained by applying the GA-ANN model, predicting the
velocity at the open channel junction is presented.",
-
keywords = "genetic algorithms, genetic programming, Artificial
neural network, Neuron number determination, Open
channel junction, Velocity prediction",
- }
Genetic Programming entries for
Hossein Bonakdari
Amir Hossein Zaji
Citations