Sentence examples for minimizing the model from inspiring English sources
The main purpose is to improve fault detection performances in a closed-loop framework by minimizing the model uncertainties effect on the residual generation.
The system is also capable to optimize the distillation process with minimizing the model output error and maximizing the required oil production rate with respect to control parameter values.
In our case, through minimizing the model size and the error, it will produce an array of equally good models that are not dominated by other models.
In the case of SVM-BD, the fixed regularization parameter defines the trade-off between the goal of minimizing the training error (i.e., the loss) and minimizing the model complexity to avoid overfitting.
In Equation 8b, ρ μ (w ij = ω) is a mean field derived change to the probability distribution of the locally optimized parameter, towards minimizing the model error (CSSE(W)).
In the adjusted model, the optimal span size (determined by minimizing the model AIC) increased, indicating less variability, and the global p-value was not consistent with departures from a flat pattern of ASD prevalence.
Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique.
A modified genetic algorithm was also developed and used as an optimization tool to minimize the model error.
This tutorial describes how to select model parameters such as the number of topics by a (computationally intensive) tuning procedure, which searches for the parameters that minimize the model's perplexity on held-out data.
A Λ-optimal design was introduced to minimize the model parameter estimation error and to maximize the performance of the fermentation process.
The method produces a low-order model after three consequent steps: construction of an optimal basis functions set with respect to model error; computing the model coefficients with the Galerkin approach; calibration of the coefficients to minimize the model error.
