Optimal Representation of Experimental Data

Computer Science – Learning

Scientific paper

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Scientific paper

No single state space reconstruction of experimental data is optimal for all scientific endeavors. This thesis is all about the coordinates used in reconstruction, the goals of reconstruction, and the criteria used to select optimal reconstructions. A learning algorithm is described, which when given a space of possible coordinates and an optimality criterion selects the best state space reconstruction. This method is applied to specific problems: modeling nonuniformly sampled data, computing the inferable dimension of a data set, and model-based control.

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