The computation of damping ratios is based on the model structure of linear second-order oscillators and the pressure spectrum.
This paper presents a new approach to the identification of pH processes based on the Wiener model structure (a dynamic linear element in series with a static nonlinearity).
The proposed approach is based on the sequential comparison of the AIC, and it can be applied to find the model structure of general linear time-invariant discrete-time systems.
As the data conformed to a repeated measures model structure a general linear mixed model (Proc Mixed, SAS Institute, Cary, North Carolina, USA) was used to assess the significance of maternal diet, vaccine (primary or secondary), day (relative to day of vaccination) and associated interactions on the serum concentrations of the acute phase proteins.
A control system design procedure is proposed for a widely applicable class of discrete-time, non-linear systems in which the system nonlinearities are incorporated into a linear model structure in the form of State-Dependent Parameter (SDP) functions.
This observation motivates to choose a local-linear model structure for this study, which consists of a network of parallel linear submodels.
Moreover the algorithm is applicable to any linear model structure, including dead time and non-minimum phase systems.
For all models, we used a linear model structure since the dependent variable was continuous (emotional closeness or change in emotional closeness).
Using a linear model structure allowed for comparability across the different models in this paper, and with previous literature (e.g., Cummings et al. 2006).
Cumomer- and EMU-based approaches are numerically stable as they inhere a (quasi-) linear model structure (Antoniewicz et al., 2007; Wiechert et al., 1999).
