Applying the updated PDB_REDO to the oldest, but also to some of the newest models in the PDB, corrects existing modeling errors and brings these models to a higher quality, as judged by standard validation methods.
The system noise is a tool that accounts for all the unknown phenomena that are not captured by the deterministic model, for example approximations, modeling errors, and oversimplifications.
Since the EPAS system has nonlinear frictions, component deviations and load disturbances, its linearized model is subject to significant modeling errors and external disturbances.
The aim of this work is twofold: (1) to propose a probabilistic model for the fluid structure interaction considering modeling errors and (2) to analyze the stability and reliability of the stochastic system.
There are many potential sources for discrepancies: different usage of the building in model and reality, design, construction and commissioning deficiencies, software limitation and modeling errors and different weather in reality than in the model are the factors typically considered.
The goal herein is to understand where modeling errors and uncertainty originate from, and obtain model predictions that are statistically consistent with their respective measurements.
The paper shows that direct tyre force sensing has mainly two advantages over traditional model-based estimators: primarily, it avoids the use of tyre models, which are heavily affected by uncertainties and modeling errors and secondarily, providing measurements on the road plane, it is less prone to errors introduced by roll and pitch dynamics.
First, GPR-derived soil moisture uncertainties were computed from GPR data inversions, according to measurements and modeling errors, and to the sensitivity of the electromagnetic model to soil moisture.
From the implementation level, any performance model is subject to two kinds of inaccuracies: intrinsic modeling errors and process variability.
Performance and robustness of these controllers are affected by the inevitable modeling errors, and parameter adaptation is a technique to robustify the model-based controllers.
This paper considers the identification of linear approximate models subject to three sources of uncertainty: additive observation errors, multiplicative modeling errors, and truncation errors.
