Sentence examples for modeling of time from inspiring English sources

T-S fuzzy model is adopted for the modeling of time delayed chaotic drive and response systems.

This paper develops a novel boundary meshless approach, Laplace transformed boundary particle method (LTBPM), for numerical modeling of time fractional diffusion equations.

We show that the modeling of time variable gravity improves the orbit solutions, at least for the GRACE period where time variable gravity is sufficiently accurately observed by this mission.

The typical signal processing applications based on higher-order cumulant (HOC) mainly include the system identification and modeling of time series analysis [10], adaptive estimation and filtering [11], signal reconstruction [12], signal detection [13], harmonic retrieval [14], image processing [15], blind deconvolution and equalization [16], and array signal processing [17].

We used the Box-Jenkins approach to ARIMA (p, d, q) modeling of time series [ 20].

After the above modeling of time varying effects, the final multivariate model was built.

OSAMNN can adapt its architecture to realize online modeling of time-varying nonlinear input output maps.

A new computational approach for the mixed-dimensional modeling of time-harmonic waves in elastic structures is proposed.

The proposed method is then illustrated with applications to four data sets, one of which involves joint modeling of time-to-event and longitudinal data (Section Examples).

While our recent technique [1] addresses the modeling of time-delays occurring in genetic interactions, the issue of large-scale GRN modeling still remains.

Moreover, the use of Cox PH in joint modeling of time-to-event and longitudinal data (Wulfsohn and Tsiatis 1997) usually leads to an underestimation of the standard errors of the parameter estimates (Hsieh et al. 2006; Rizopoulos 2012), and therefore most methods for joint modeling are based on parametric response distributions (Hwang and Pennell 2014).