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Covariance-Invariant Mapping of Data Points to Nonlinear Models
  • Wolfgang Grimm
Wolfgang Grimm
Independent Research and Consulting
Author Profile

Abstract

A centroid- and covariance-invariant deterministic mapping of sets of discrete data points to nonlinear models is introduced. Conditions for bijectivity of this mapping are developed. Since the mapping can be accomplished by look-up tables for the special case of equally-spaced data, the resulting mapping algorithm is considered computationally fast. This could be attractive for real-time operations.

Peer review status:UNDER REVIEW

15 Nov 2021Submitted to Mathematical Methods in the Applied Sciences
16 Nov 2021Assigned to Editor
16 Nov 2021Submission Checks Completed
26 Nov 2021Reviewer(s) Assigned