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We also compare reconstructions from the Boltzmann Poisson (BP) model to those from the classical drift diffusion-Poisson (drift diffusion-Poissonat measurements are generateDDPith the BP model.
The average temperature for the other sample groups was best predicted by the BP model.
The correlation coefficient (R2) of the BP model was lower than that of ELM model.
The BP model is hypothesized to account for bubble stability, thus providing physical solutions.
The paper demonstrates how to design a decision tool based on the BP model and provides examples.
The effects of grid resolution, bubble pressure (BP) model, and drag coefficient models on the numerical predictions are examined.
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Additionally, compared with the other three general models, i.e. BP neural model, physical model and CK model, the multiple linear regression model is more reasonable and effective.
MSE and R2 of the ACOR-BP model in testing samples are 0.0051 and 0.952, respectively.
Results show that the optimized GA-BP model can effectively predict the long-term skid resistance of EAM, and the long-term skid resistance has a significant negative correlation with binder content and shape characteristic of aggregate gradation.
According to the results, the PSO algorithm successfully improved the RBFN while the RBFN-PSO model's generalization capacity enhanced, with RMSE of 0.071 compared to the RBFN-BP model with RMSE of 0.114 in the testing dataset.
Secondly, using the database, an optimized GA-BP neural network model (i.e. GA-BP model) was established to predict the long-term skid resistance, and then a comprehensive sensitivity analysis was conducted to explore the effect of input parameters on the skid-resistant evolution based on the trained neural network.
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