"model parameter optimization" is a correct and usable phrase in written English.
It is used to describe the process of finding the best values or settings for the parameters of a model, which is a mathematical representation of a system or process. Example: The researchers conducted model parameter optimization to determine the most accurate values for the variables in their climate prediction model.
Model parameter optimization, and tree searches using fixed optimized parameters, were done using a successive approximations approach [ 48].
Figure 3 and 4 show model parameter optimization and consecutive training of a model with the optimized parameters.
Most proposed metaheuristics for feature selection and model parameter optimization are based on a two-termed Loss+Penalty function.
In this study, we propose to integrate pair-wise distance metric learning as the regularization of model parameter optimization.
However, few methodologies have focused on the importance of model parameter optimization or data pre-processing, resulting in undesirable forecasting performance.
A generalized dynamic procedure is proposed for sampling-based LES which allows for model parameter optimization and does not require a detailed analysis of the discretization error.
Summary: The Infobiotics Workbench is an integrated software suite incorporating model specification, simulation, parameter optimization and model checking for Systems and Synthetic Biology.
As most adaptive methods, the RLEM model includes three types of parameters: Model parameters, optimization parameters, and evaluation parameters.
The writer lists several structure forms of aircraft undercarriage's bumper, recounts the key technologies and existing major problems of the design for the modeling, parameter optimization, control, simulation and experimental validation, and finally makes some discussions to the developing trend of the design for undercarriage buffer system.
The multi-agent system approach to power plant control provides many benefits in that it allows multiple computationally intensive tasks to be performed in parallel to provide effective real-time control, such as training and adapting artificial neural network models, parameter optimization, and system monitoring.
Zheng et al. [11] adopted global sensitivity analysis to perform on a marine ecosystem dynamic model, and proposed the model's parameter optimization strategy and applied it to the Sanggou Bay.
