Sentence examples for model at multiple from inspiring English sources

We tested the model at multiple institutions.

Here we have connected the output of our DoG model at multiple scales, to some prominent perceptual grouping principles such as continuity, connectivity and similarity, which are high-level perceptual explanations of our final percept.

This would correspond to simply replicating the model at multiple scales.

So, the different parts of the model at multiple levels are highly interconnected and influence each other in various ways (see Figure 4).

Parameter estimation for deterministic models is frequently done by running the model at multiple values of the input parameters, constructing an approximator to the model, and using the approximator inside a numerically intense loop that examines many trail values for the input parameters [ 5– 8].

This project aims to apply modern software engineering techniques to create a flexible high performance neural modelling environment, which will allow rigorous exploration of model parameter effects, and modelling at multiple levels of abstraction.

In LANDIS, vegetation heterogeneity is modeled at multiple hierarchical levels from the landscape to the pixel.

The idea of modeling at multiple scales the phenomena behavior has become a useful tool in pure mathematics, applied mathematics physics and so on.

In this paper, an Integrated Geomorphological Adaptive Neuro-Fuzzy IGANFISce SystemodelANFIS) model conjugated with C-means clustering algorithm was used for rainfall runoff modeling at multiple stations of the Eel River watershed, California.

Not only does the use of the LINAC for that purpose allow for unlimited quantity of irradiated blood samples, it also avoids collecting extra blood from cancer patients and the use of animal models at multiple validation phases of the study.

This paper presents a Bayesian hierarchical formulation for simultaneously calibrating aquatic biogeochemical models at multiple systems (or sites of the same system) with differences in their trophic conditions, prior precisions of model parameters, available information, measurement error or inter-annual variability.