This includes de-noising, template fitting, landmark identification, and statistical shape analysis.
Because of the higher modal density, depending on the parameters (and the chosen fitting procedure) identification and attribution becomes more difficult.
The input data is based on experimental results or inverse fitting, with a clearly identification and justification.
An estimation of these parameters is needed in order to obtain quantitative predictions; this is the next step, which is commonly known as parameter estimation or identification, data fitting, or model calibration [ 1, 4- 6].
In our approach to data-supported computational modelling of biological networks, we take the view that one should follow an iterative procedure that includes model identification (model fitting), model discrimination (in which a new experiment is designed) and model invalidation (using the new experimental data).
By analyzing the catenary fittings using intelligent image identification algorithms, the image-based non-contact catenary methods are able to detect geometry parameters of pantographs and catenaries, as well as to recognize the faults of the pantograph catenary system [75].
The autoregressive (AR) model is widely used in electroencephalogram (EEG) analyses such as waveform fitting, spectrum estimation, and system identification.
CdZnTe detectors, often exhibit asymmetric peak shapes, particularly at high energies making automated peak fitting methods and sophisticated isotope identification programs difficult to use.
After identification of best fitting parameters, all samples were scanned with this protocol.
Better matching period models were extrapolated to AP and APC models in order to achieve better fitting and to overcome the "identification problem" of a full APC models.
