@TechReport{ it:2017-007,
author = {Torbj{\"o}rn Wigren},
title = {{MATLAB} Software for Nonlinear and Delayed Recursive
Identification - Revision 1},
institution = {Department of Information Technology, Uppsala University},
department = {Division of Scientific Computing},
year = {2017},
number = {2017-007},
month = apr,
note = {The software package can be downloaded from
\url{http://www.it.uu.se/research/publications/reports/2017-007/RecursiveNonlinearNetworkedIdentificationSW.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
an output error identification algorithm. The algorithm is
based on a continuous time, structured black box state
space model of a nonlinear system. 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.}
}