Covariance-Invariant Mapping of Data Points to Nonlinear Models
- Wolfgang Grimm
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.15 Nov 2021Submitted to Mathematical Methods in the Applied Sciences 16 Nov 2021Submission Checks Completed
16 Nov 2021Assigned to Editor
26 Nov 2021Reviewer(s) Assigned
11 Mar 2022Review(s) Completed, Editorial Evaluation Pending
12 Mar 2022Editorial Decision: Revise Major
06 Jun 20221st Revision Received
07 Jun 2022Submission Checks Completed
07 Jun 2022Assigned to Editor
07 Jun 2022Reviewer(s) Assigned
08 Aug 2022Review(s) Completed, Editorial Evaluation Pending
09 Aug 2022Editorial Decision: Revise Minor
09 Aug 20222nd Revision Received
10 Aug 2022Submission Checks Completed
10 Aug 2022Assigned to Editor
10 Aug 2022Reviewer(s) Assigned
10 Aug 2022Review(s) Completed, Editorial Evaluation Pending
16 Aug 2022Editorial Decision: Revise Minor
21 Aug 20223rd Revision Received
22 Aug 2022Submission Checks Completed
22 Aug 2022Assigned to Editor
22 Aug 2022Reviewer(s) Assigned
26 Aug 2022Review(s) Completed, Editorial Evaluation Pending
27 Aug 2022Editorial Decision: Accept
14 Sep 2022Published in Mathematical Methods in the Applied Sciences. 10.1002/mma.8712 
