A novel neural modelling method, namely 'eng-genes', is proposed for complex nonlinear dynamic engineering systems.
We suggest employing two commonly used model selection methods, namely MDL and CV, for making such choices.
To this end, we use two model selection methods, namely minimum description length (MDL) [15] and cross-validation (CV) [16, 17].
More recently, probabilistic model-based methods, namely the maximum likelihood (ML) and the Bayesian approaches have grown to prominence due to their statistical properties and inferential powers.
Based on the engine models, two optimization methods, namely SA (simulated annealing) and PSO (particle swarm optimization), are employed and a flexible objective function is designed to determine the optimal biodiesel ratio subject to various user-defined constraints.
In order to take into account the abovementioned issues, we estimated the panel gravity model using four different methods namely ordinary least squares (OLS), scaled ordinary least squares (SOLS), Tobit model and fixed effects (FE), following Ullah (2012).
We estimated the panel gravity model using four different methods namely ordinary least squares (OLS), scaled ordinary least squares (SOLS), Tobit model, and fixed effect (FE), following Ullah (2012).
The major energy and climate policy modeling is done via quantitative methods, namely bottom-up and top-down models.
In order to model soil-tool interaction two methods namely; finite element method (FEM) and discrete element method (DEM) have been used.
To develop such a model, we used two statistical methods, namely a recalibrated SAPS II model based on logistic regression and the technique of recursive partitioning analysis (RPA).
