Sentence examples for whose estimation from inspiring English sources
Consequently, in order to estimate both z and θ, we propose to use an adaptive observer whose estimation state error does vanish asymptotically.
The last thing the region needs for genuine, long-term economic health is a generation of think-they-can-dos whose estimation of their own success is based on whether they got that mythical £60k.
The proposed approach is comprehensively evaluated using six artificial benchmarks, within several outlier contamination levels, and two datasets related to process industry systems (pH neutralization and heat exchanger), whose estimation data undergo large contamination rates.
To estimate both the continuous and discrete states, a novel observer is designed for each mode whose estimation error is not affected by continuous faults and sensitive to discrete mode transitions.
I expand the equations whose estimation underlay the results shown in Tables 2 and 3 to include interactions of the average looks of the candidates in the game or election with the individual participant's or candidate's looks.
This is due to the fact that the a2 estimation is influenced by the estimation accuracy of three parameters, namely A2, a3, and θ, whose estimation RMSEs contribute to the estimation RMSE of a2.
The detection of the domain of applicability defines compounds whose estimations can be accepted with confidence.
Potential derivatives with higher predicted activity values than the template were identified and a detailed analysis on the models applicability domain defined the designed compounds, whose estimations can be accepted with confidence.
Since delivery delays of messages originated by MN3 and SN4 are built over the delays of nearer nodes MN1 and/or SN2, and since they are consequently obtained as sum of independent contributions, whose estimations suffer from unavoidable errors, the difference between minimum (or, equivalently, maximum) delays under experimental and theoretical analysis tends to increase.
The former plays a key role in demonstrating the near optimality of the newly derived joint ML-based estimator, whose performance (estimation error) comes within 1 dB or less from the bound.
The work in [15] links the acoustic vector-sensor array parameter estimation problem to trilinear model and derives a PARAFAC algorithm whose angle estimation performance is better than the ESPRIT algorithm.
