@TechReport{ it:2022-002,
author = {Torbj{\"o}rn Wigren},
title = {{MATLAB} Software for Nonlinear and Delayed Recursive
Identification - Revision 2},
institution = {Department of Information Technology, Uppsala University},
department = {Division of Scientific Computing},
year = {2022},
number = {2022-002},
month = jan,
note = {Revised version of nr 2017-007. The software package (last
updated 2022-09-12) can be downloaded from
\url{https://www.it.uu.se/research/publications/reports/2022-002/RecursiveNonlinearNetworkedIdentificationSW-r3.zip}.}
,
abstract = {This report is the user's manual for a package of MATLAB
scripts and functions, developed for recursive prediction
error identification of nonlinear state space systems. The
identified state space model incorporates delay, which
allows a treatment of general nonlinear networked
identification, as well as of general nonlinear systems
with delay. The core of the package is an implementation of
two output error identification algorithms. The algorithms
are based on a continuous time, structured black box state
space model of a nonlinear system. The present revision
adds a new algorithm, where also the output is determined
via a parameterized measurement equation in the states and
inputs. The software can only be run off-line, i.e. no true
real time operation is possible. The algorithms are however
implemented so that true on-line operation can be obtained
by extraction of the main algorithmic loop. The user must
then provide the real time environment. The software
package contains scripts and functions that allow the user
to either input live measurements or to generate test data
by simulation. The scripts and functions for the setup and
execution of the identification algorithms are somewhat
more general than what is described in the references. The
functionality for display of results include scripts for
plotting of e.g. data, parameters, prediction errors,
eigenvalues and the condition number of the Hessian. The
estimated model obtained at the end of a run can be
simulated and the model output plotted, alone or together
with the data used for identification. Model validation is
supported by two methods apart from the display
functionality. First, a calculation of the RPEM loss
function can be performed, using parameters obtained at the
end of an identification run. Secondly, the accuracy as a
function of the output signal amplitude can be assessed.}
}