Sentence examples for modelling errors is from inspiring English sources

In this paper, the robust fault detection filter design problem for uncertain linear time-invariant (LTI) systems with both unknown inputs and modelling errors is studied.

A novel method for the robust identification of interpretable fuzzy models, based on the criterion that identification errors are least sensitive to data uncertainties and modelling errors, is suggested.

At each step of design, a feedback controller strengthened by nonlinear damping terms to counteract modelling errors is designed to guarantee input-to-state practical stability of the corresponding subsystem, and then parameter adaptions are introduced to reduce the ultimate error bound.

On the other hand, EM-2, which takes into account correlation of the modelling errors, is capable of estimating the optical parameters with good accuracy even if the background values are 30% off from the expected ones.

Design sensitivities calculated directly from measured data are more accurate than those calculated from analytical or finite element models since structural modelling errors are inevitable due to the complexity of almost all engineering structures.

Therefore, in the controller design, the approximations, assumptions and other modelling errors are largely eliminated.

Modelling errors are estimated using the ASME V&V20 procedure which requires numerical and experimental data with their respective uncertainties.

If both factors are not differently identified when the modelling errors are learned and reflected in the adaptation of a setup control, controlling accuracy gets worse.

The approximation error approach for the treatment of modelling errors was introduced in [ 11, 12].

The bias and variance of the estimated model parameters are analysed and a frequency domain bound on the modelling error is estimated.

The average modelling error is 7.64%, forecasting error 14.96%, and random error 7.32%.