NONLINEARITY, SCALE, AND SENSITIVITY FOR PARAMETER ESTIMATION PROBLEMS: SOME IMPLICATIONS FOR ESTIMATION ALGORITHMS
Trond Mannseth, Kari Brusdal, Alv-Arne Grimstad, Jan‐Erik Nordtvedt, Geir Nævdal · 1999
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