Sentence examples for large parameter estimation from inspiring English sources

Small errors in the depth of the overcoring samples lead to large parameter estimation errors.

The fact that island-based evolutionary algorithms can be highly reliable function optimisers in large parameter estimation problems has been well established [ 40].

In this paper, we demonstrate the efficiency and versatility of PGO with two different applications: (1) the parallelization of a large scale parameter estimation problem associated with modeling water flow in a heterogeneous deep vadose zone; (2) the parallelization of a complex simulation-optimization procedure for searching for an optimal groundwater remediation design.

In this work we propose a new hybrid global method, based on the combination of an evolutionary search strategy with a local multiple-shooting approach, which offers a reliable and efficient alternative for the solution of large scale parameter estimation problems.

An enhanced scatter search (eSS) with combined opposition-based learning algorithm is proposed to solve large-scale parameter estimation in kinetic models of biochemical systems.

For example, recent developments on model-centric technologies to support plant operations based on advanced process modelling technologies opened the opportunities for performing large-scale parameter estimation – data reconciliation applications in complex dynamic industrial environments.

A large-scale parameter estimation protocol was employed to capture multiple parameter sets that describe the experimental data of phenotype A (wild-type C57BL/6J mice).

Our results demonstrate that large-scale parameter estimation can be successfully performed for nonlinear dynamic gene regulatory networks using sparse, noisy microarray data.

A large-scale parameter estimation protocol was employed to capture multiple parameter sets describing the biological system of phenotype A. First, parameter regions were identified that are most likely to describe the experimental data.

A large-scale parameter estimation protocol was employed to capture multiple parameter sets describing the biological system of phenotype A. A collection of 2909 acceptable parameter sets were obtained that describe the experimental data.

This reasoning applies for the case of new arbitrary problems, but we should add that for the particular large-scale parameter estimation problems considered above, the default recommended values have shown a good performance.