Sentence examples for identification in terms from inspiring English sources

17. " Power Spectrum Identification in Terms of Rational Models," (with S. A. Tretter), IEEE Trans.

6. "Power Spectrum Identification in Terms of Rational Models," (with S. A. Tretter), Proc.

In addition, we provide sufficient conditions for point identification in terms of support conditions.

Computer simulations demonstrate that the performance of the nonlinear filter proposed is superior to the RBF for nonlinear dynamic system identification in terms of convergence speed, steady state error and tracking capability.

We also investigate the way these reciprocal data, eventually corrupted by measurement errors, influence model parameter identification in terms of: (a) the convergence of the inverse model, (b) the optimal values of parameter estimates, and (c) the associated estimation uncertainty.

In this paper, by analyzing in-depth the relationship between observed fractures in the cores and well logging data, we proposed a new method of fracture identification in terms of deep-shallow laterolog curves as well as their amplitude difference and micro-electrode logging curves as well as its amplitude difference.

Table 6 presents the effects of the developed semi-fragile digital speech watermarking system on the performance of the GMM speaker identification system in terms of recognition rate.

Using PCRLB, a systematic procedure is developed to analyse the estimates delivered by Bayesian identification methods in terms of bias, MSE, and efficiency.

The result also demonstrates that the proposed method is less vulnerable with a smaller number of identification regions in terms of association performance.

Feature selection after data preparation, we select the most contributing features and exclude those that are highly correlated with them in order to improve the driver identification performance in terms of accuracy and speed.

Fig. 7 Comparison of identification accuracy, in terms of a MAP, b MaRR, c TOP10, and d MeanRank, and classification efficiency, in terms of e TNR, f CA, and g ACA, obtained after late fusion based on different learning method combinations on different datasets.