Open channel junction velocity prediction by using a hybrid self-neuron adjustable artificial neural network

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@Article{Bonakdari:2016:FMI,

• author = "Hossein Bonakdari and Amir Hossein Zaji",
• title = "Open channel junction velocity prediction by using a hybrid self-neuron adjustable artificial neural network",
• journal = "Flow Measurement and Instrumentation",
• volume = "49",
• pages = "46--51",
• year = "2016",
• ISSN = "0955-5986",
• DOI = "doi:10.1016/j.flowmeasinst.2016.04.003",
• URL = "http://www.sciencedirect.com/science/article/pii/S0955598616300309",
• 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