Sentence examples for models namely linear from inspiring English sources

The regression equation of the designed experiment was obtained by applying four models namely linear, interactive, quadratic and cubic models which were fitted to the experimental data obtained from the design system.

Then, the proposed model was compared to the well-known IFT models, namely Linear Gradient Theory (LGT), Density Gradient Theory (DGT), and Parachor approaches combined with the Volume Translated Predictive Peng Robinson Equation of State (VT-PPR EOS).

The performance of the linear and nonlinear Hunt Crossley models in simulating impact force between concrete bodies is compared against other existing pounding models, namely: linear viscoelastic, nonlinear viscoelastic and modified linear viscoelastic models.

Moreover, three different classification models, namely linear classifiers, neural networks and support vector machines, were employed to investigate the true efficiency of each one of them, as well as the overall complexity of the diagnostic task of mammographic tumor characterization.

Three responsive load models, namely, linear, exponential and potential demand functions are proposed.

The control design and performance are then evaluated using three control algorithms applied to the reduced-order model, namely, linear quadratic regulator (LQR), linear quadratic gaussian (LQG) and derivative feedback.

This study investigates the differences between using a non-linear global surrogate model (namely, least-squares support vector machines) and a linear piece-wise surrogate model (namely, linear nearest neighbour interpolation), as well as the use of sequential sampling algorithm as a means of improving overall surrogate accuracy.

The mechanical interactions within the digital samples were simulated through four contact models, namely the linear contact stiffness model, the Burger's model, the slip model, and the contact-bond model.

Here, we employed two graphical probabilistic models, namely the linear conditional random field model and multivariate hidden Markov model, to mark gene regions with different states based on recurrent and spatially coherent character of these eight marks.

The structure proposed in equation (6) was tested and validated by comparing the outcomes of two different modeling schemes, namely linear time series modeling and fuzzy modeling.

Three typical manifold learning models, namely, ISOMAP, Local Linear Embedding (LLE) and principle component analysis (PCA), were employed, and their results were compared.