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Figure 1 Equation-based and simulation-based approaches for generating performance models compared with multiple metrics.
Numerical simulations were compared with multiple TDR sensor measurements from different locations and depths.
Artificial neural networks (ANN) were applied in the optimization and compared with multiple linear regression (MLR).
In this study, the artificial neural network results were also compared with multiple linear regression results.
The performance of the ANN model is compared with multiple nonlinear and linear regression models.
Lastly, the models were compared with multiple linear regression (MLR) and a previous random forest regression (RFR) model.
When compared with multiple regression models, ANNs provide improved flood estimates that can be used by engineers and hydrologists.
However, data comparing the efficacy of medical abortion for singleton gestations as compared with multiple gestations are limited.
Compared with multiple turbo codes [16], the decoding latency of GLD codes is shorter due to the parallel decoding of constituent codes in each supercode.
Test results of the ANN and ANFIS models were compared with multiple nonlinear regression, multiple linear regression and existing bond strength models.
Three machine learning methods – Bayesian neural network (BNN), support vector regression (SVR) and Gaussian process (GP) – were used and compared with multiple linear regression (MLR).
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