Sentence examples for model complexity as from inspiring English sources
This paper investigates the dynamic modeling of multi-time scale DC microgrid, and a reduced-order multi-scale model (RMM) is proposed to reduce the system model complexity as well as to conserve major time scale information.
In addition, a twofold criterion based on the smoothness of the parameter prediction and the accuracy of the estimation is introduced in order to investigate the required model complexity as well as to potentially rule out ineffective terms during the identification procedure (on-line).
The methods for identification of static nonlinearities indeed do not require the same level of model complexity as methods used for nonlinear systems with memory or with gain control.
Our bootstrapping approach reveals that the variability of the estimators increased with model complexity, as expected.
We then fitted various time-to-event regression models with increasing model complexity as described below.
AIC and BIC measures closely tracked each other for all models, and showed an increase with increasing model complexity, as expected.
The concept behind this methodology involves the treatment of model complexities as disturbances, the time-domain behavior of which can be captured in a dynamic model that is used to develop control commands that cancel or minimize the effects of the time variations of linear dynamic system complexities.
Incorporating model complexity such as that required by structured populations with stage-specific vital rates affects precision and accuracy when estimating latent population variables and predicting population dynamics.
A large number of variables usually cause over-fitting problems by increasing model complexity and as a result, the trained model tends to be sensitive to noise samples.
The rate of improvement in the model performance decreased as model complexity increased.
The important points in objectively selecting model structures for regionalisation are proposed as: performance in calibration and validation, trade-off between high and low flow performance, and model complexity (here represented as the number of parameters).
