If g n → 0 as n → ∞, i.e. if all the uncertainty sources of noise/unmodeled dynamics and the identification error between the true process vanish asymptotically and the identifier { T ˆ ˜ n } → 0 uniformly, then { T ˆ n } → T ˆ uniformly, T ˆ ′ = 0, then | D n 0 − D n | → 0 as n → ∞ and { D n 0 }, { D n } → D = 0.
This is measured by the suboptimality level of the identified model set, i.e. the ratio between the achieved identification error and the minimal one.
Change-based models are therefore better able to explain the correlation between target identification errors and the structure of flanking elements [ 9, 16, 22 25].
The changes we observed in crowded target appearance are consistent with the correlation between target identification errors and the identity of flanking elements [ 9, 16, 22 25], suggesting that these systematic effects reflect a genuine change in the target representation rather than behavioral strategies aimed at overcoming uncertainty.
An analytical link is available between the stability margin and identification errors for the proposed controller.
Then we calculate the root-mean square error between the identification result and the result of inverse Jacobian method, respectively, as shown in Fig. 2.
In this paper, the upper bound of relative load identification error in frequency domain is derived.
This includes the online monitoring of responses allowing the identification of errors, as per earlier error-detection theories, and also the detection of conflict between different possible responses to a stimulus, event, or situation.
An identification error analysis reveals how structural responses affect the identification accuracy.
The outcomes between the control group and those who died were not blinded, which could affect the identification of errors despite efforts to remain objective.
