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However, in the case where multiple parameter vectors are to be measured, and the measurement model of each measured parameter vector is known, then the general EKF-based framework of AS presented[6] can be used.
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The identification of the relationship among the robustness to multiple parameter perturbation (Equation 13), the sensitivity distribution, and the parameter solution vectors could provide a basis to assume a potential mechanisms generating robustness.
A multiple parameter optimization viz.
The robustness to multiple parameter perturbation greatly depends on the values of kinetic parameters.
Parameter optimisation to fit such data can reveal multiple parameter sets spanning large tracts of parameter space.
Set initial values for parameter vectors.
This enabled different combinations of parameter vectors to be explored.
B, Γ and D denote the accompanying parameter vectors.
This produces a parameter grid that contains a total of 625 different parameter vectors.
The parameter "pool" φ is therefore simply a concatenation of all model parameter vectors.
Starting from each of the 15 successful parameter vectors chosen from the DE run, we introduced one of the parameter perturbations to create a "new" parameter vector.
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