Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
The reliable solution of nonlinear parameter es-
timation problems is an important computational
problem in many areas of science and engineering,
including such applications as real time optimization.
Its goal is to estimate accurate model parameters that
provide the best fit to measured data, despite small-
scale noise in the data or occasional large-scale mea-
surement errors (outliers). In general, the estimation
techniques are based on some kind of least squares
or maximum likelihood criterion, and these require
the solution of a nonlinear and non-convex optimiza-
tion problem. Classical solution methods for these
problems are local methods, and may not be reliable
for finding the global optimum, with no guarantee
the best model parameters have been found. Interval
arithmetic can be used to compute completely and
reliably the global optimum for the nonlinear para-
meter estimation problem. Finally, experimental re-
sults will compare the least squares, l2, and the least
absolute value, l1, estimates using interval arithmetic
in a chemical engineering application.
