In this study, we consider structural uncertainty as uncertainty associated with assumptions of model structure, including distance between states, time assumption for similarity measure, assumptions of fuzzy ID3, and distance between states and centroid for changes percentage.
A previous study [ 58] suggested a two-stage process for assessing the PLS model structure, including (1) the measurement model and (2) the structural model.
Moreover the algorithm is applicable to any linear model structure, including dead time and non-minimum phase systems.
The numerical solution approach and the practical development of a suitable model structure, including the required control circuits, are explained.
The subproblems of parameter estimation, model structure (including basis function) selection, and model validation are discussed, and recent developments are outlined.
A particular feature of the AWBM is the development of model-specific calibration procedures based on the model structure, including a graphical analysis of rainfall and runoff data, multiple linear regression and an automatic self-calibrating procedure.
In the Results section, the defined test treatment strategies and model structure (including assumptions and likely data sources) are given in detail.
A defined model structure, including the test treatment strategies, and the selection of a published diabetes model appropriate for the economic evaluation of strategies to identify patients with monogenic diabetes.
The detailed model structure including a hypothetical S. aureus vaccination component is shown in Figure 1, resulting in a highly versatile modelling framework, with enhanced options for conducting differential analyses.
