Applying nonlinear models to estimation and forecasting economic models are now becoming more common, thanks to advances in computing technology. Artificial Neural Networks (ANN) models, which are nonlinear local optimizer models, have proven successful in forecasting economic variables. Most ANN models applied in Economics use the gradient descent method as their learning algorithm. However, t...

The classic data envelopment analysis (DEA) models have overlooked the intermediate products, internal interactions and the absence of data certainty; and deal with analyzing the network within the “Black Box” mode. This results in the loss of important information and at times a considerable modification occurs in efficiency results. In this paper, a Three-stage network model is considered wit...

the purpose of this paper is to studying nonlinear k-ε turbulence models and its advantages in internal combustion engines, since the standard k-ε model is incapable of representing the anisotropy of turbulence intensities and fails to express the reynolds stresses adequately in rotating flows. therefore, this model is not only incapable of expressing the anisotropy of turbulence in an engine c...