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• W266456789 abstract "Both sensitivity and nonlinearity are important for the efficiency of an estimation algorithm. Knowledge of a general nature on sensitivity and/or nonlinearity for some class of models can perhaps be utilized to improve the estimation efficiency for this class. For an ODE model, a correlation between high nonlinearity, low sensitivity, and small-scale perturbations, has been reported. Also, it was found that representing the unknown function by a multi-scale basis lead to faster estimation convergence than use of a single-scale local basis. This was explained referring to the above-mentioned correlation. Recently, the existence of such a correlation for a large class of nonlinear models, including the above-mentioned ODE model, was found. Here, we further investigate into utilization of the correlation" @default.
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• W266456789 date "1999-01-01" @default.
• W266456789 modified "2023-09-27" @default.
• W266456789 title "NONLINEARITY, SCALE, AND SENSITIVITY FOR PARAMETER ESTIMATION PROBLEMS: SOME IMPLICATIONS FOR ESTIMATION ALGORITHMS" @default.
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