Sentence examples for model identification task from inspiring English sources

First, we show that the proposed solution outperforms the state-of-the art approaches used for the model identification task.

The preliminary results presented in this paper show that designing experiments in parallel, rather than sequentially, can substantially decrease the time and effort required by the model identification task for a microbial growth model.

Since model identification is a task that consumes large amounts of experimental data, an iterative identification procedure is proposed which is intended to accurately compute model unknowns while reducing experimental cost.

The well-testing model identification is a challenging task due to non-uniqueness of the pressure responses generated by different reservoir models.

If engineers do not pay attention to these issues, the productive life of reservoir and subsequently the efficient production of a reservoir will be limited (Bodin and Lopes, 2010).The well-testing model identification is a challenging task due to the non-uniqueness of the pressure responses generated by different reservoir models.

In the case when Γ consists of many Pareto points, the model identification becomes a difficult task: the Tanimoto similarity coefficient (as well as other fingerprint similarity measures) between chemical compounds may not be correlated enough with their activity partially contradicting the similarity hypothesis [32] (see the end of this section for a detailed example).

The application of computational methods to optimize model parameters regarding the fit error has therefore become an important task in the model identification process [ 28- 31].

Although apparently simple, non-linear model identification is usually a very challenging task, due to the usual lack of identifiability, either practical or, in the worst case, structural.

Unlike other GRN inference methods for time-series data, such as state-space models [ 13], dynamic Bayesian networks [ 14] and other ODE-based methods [ 17], the Inferelator can be used for the more limited target identification task since it models the single layer target-regulator network in a decoupled manner.

The nonlinear character and the usually large number of parameters in biological mathematical models make model identification from experimental data a rather complex task.

Empirical model identification for biological systems is a challenging task due to the combined effects of complex interactions, nonlinear effects, and lack of specific measurements.