WIT Press


Processing Superposed Measurement Data By Regression Algorithms

Price

Free (open access)

Paper DOI

10.2495/CMEM950071

Volume

12

Pages

7

Published

1995

Size

518 kb

Author(s)

H. Moeck

Abstract

The structure of physical phenomena usually results from interrelated influences. Assuming a corresponding decomposition model is known, the problem of measurement data analysis will often be reduced to fitting the data by superposed single influence functions with respect to a fixed period of time or a common spatial region. But since in many cases the influences have unknown shifts of time and position, additional relations with respect to the domain have to be taken into account. We present a modular system for fitting one or two-dimensional measurement data, using arbitrarily superposed model functions. The desired intrinsic shape parameters of the model functions and as the parameters of the transition of time and position are determined by special constraint minimisation procedures. There are many applications of such regress

Keywords