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Because of the need to simplify hydrological models, parameters are often included to help with modelling hydrological processes.
Design and identification of the models parameters are detailed.
Both SA methods ranked the models' parameters according to their influence on the outputs.
Using empirical models, parameters have to be estimated from experimental data.
Algorithm for local models' parameters estimation and structure adjustment has been developed.
An update of the fuzzy models parameters are obtained using Lyapunov׳s second method.
The models are redefined using the Hill family of diversities, and a procedure for estimating the models parameters' is given.
We used a neuronally plausible model to characterise the dependency of response components on the models parameters.
The effect of concentration on viscosity was observed and included in the Arrhenius and WLF models' parameters.
Behavioral dynamics are represented as Gaussian mixture models, parameters of which are identified from the recorded demonstrations.
The three models' parameters were best fit to a single force train in the trained soleus muscle (N=4).
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