The phrase "models parameters estimation" can be correct and usable in written English, as it is a common and straightforward way of describing the process of estimating the parameters of a model. It can typically be used in technical or academic writing, particularly in fields such as statistics, economics, or engineering. Example: "The first step in the analysis was to perform a rigorous models parameters estimation, using a combination of regression techniques and Monte Carlo simulations."
From the entire set of 20 song and 10 ml noise stimuli, 19 songs or 9 ml noise samples were used to estimate the models' parameters (estimation data set).
Algorithm for local models' parameters estimation and structure adjustment has been developed.
This paper comprehensively reviews PEMFC models parameters estimation methods with a specific view to online identification algorithms, which are considered as the basis of global energy management strategy design, to estimate the linear and nonlinear parameters of a PEMFC model in real time.
Using the novel PDF approximations in the ML estimator, an algorithm for model parameters estimation of the noise is initially developed.
The paper also introduces an effective method for the ARFIMA model parameters estimation and identification using Haslett and Raftery estimator and Hyndman and Khandakar technique.
Single-parameter informed analysis is applied to sinusoidal model parameters estimation described in Section 3.2.
The set-bounded joined state and grey-box model parameters estimation algorithm is validated by simulation.
Using the fatigue data, including life time and inclusion size, an FV520B-I empirical fatigue life prediction model is identified by both model fitting and model parameters estimation.
These allow the model parameters estimation based on the available experimental data, as follows: i) Phosphorylation and dephosphorylation reactions were approximated by Michaelis-Menten kinetics.
The development of metabolic networks models includes several steps which must interconnect with each other, related to biological knowledge, experimental data acquirement, mathematical modeling, parameters estimation, and model evaluation.
This paper is structured as follows: first, we describe the models, parameter estimations, and algorithms of the two methods.
