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Parameter estimation: local identifiability of parameters

American Journal of Physiology-Endocrinology and Metabolism, 1990
For biological systems one often cannot set up experiments to measure all of the state variables. If only a subset of the state variables can be measured, it is possible that some of the system parameters cannot influence the measured state variables or that they do so in combinations that do not define the parameters' effects separately.
J A, Jacquez, T, Perry
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Parameters and Parameter Tuning

2015
Chapter 3 presented an algorithmic framework that forms the common basis for all evolutionary algorithms. A decision to use an evolutionary algorithm implies that the user adopts the main design decisions behind this framework. Thus, the main algorithm setup follows automatically: the algorithm is based on a population of candidate solutions that is ...
A. E. Eiben, J. E. Smith
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Choosing Parameters

2017
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Duquesne, Sylvain   +4 more
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