100,605 research outputs found
Consistency of System Identification by Global Total Least Squares
Global total least squares (GTLS) is a method for the identification of linear systems where no distinction between input and output variables is required. This method has been developed within the deterministic behavioural approach to systems. In this paper we analyse statistical properties of this method when the observations are generated by a multivariable stationary stochastic process.
In particular, sufficient conditions for the consistency of GTLS are derived. This means that, when the number of observations tends to infinity, the identified deterministic system converges to the system that provides an optimal appoximation of the data generating process. The two main results are the following. GTLS is consistent if a guaranteed stability bound can be given a priori. If this information is not available, then consistency is obtained (at some loss of finite sample efficiency) if GTLS is applied to the observed data extended with zero values in past and future
Stable Nonlinear Identification From Noisy Repeated Experiments via Convex Optimization
This paper introduces new techniques for using convex optimization to fit
input-output data to a class of stable nonlinear dynamical models. We present
an algorithm that guarantees consistent estimates of models in this class when
a small set of repeated experiments with suitably independent measurement noise
is available. Stability of the estimated models is guaranteed without any
assumptions on the input-output data. We first present a convex optimization
scheme for identifying stable state-space models from empirical moments. Next,
we provide a method for using repeated experiments to remove the effect of
noise on these moment and model estimates. The technique is demonstrated on a
simple simulated example
Using the partial least squares (PLS) method to establish critical success factor interdependence in ERP implementation projects
This technical research report proposes the usage of a statistical approach named Partial
Least squares (PLS) to define the relationships between critical success factors for ERP
implementation projects. In previous research work, we developed a unified model of
critical success factors for ERP implementation projects. Some researchers have
evidenced the relationships between these critical success factors, however no one has
defined in a formal way these relationships. PLS is one of the techniques of structural
equation modeling approach. Therefore, in this report is presented an overview of this
approach. We provide an example of PLS method modelling application; in this case we
use two critical success factors. However, our project will be extended to all the critical
success factors of our unified model. To compute the data, we are going to use PLS-graph
developed by Wynne Chin.Postprint (published version
Prediction error identification of linear dynamic networks with rank-reduced noise
Dynamic networks are interconnected dynamic systems with measured node
signals and dynamic modules reflecting the links between the nodes. We address
the problem of \red{identifying a dynamic network with known topology, on the
basis of measured signals}, for the situation of additive process noise on the
node signals that is spatially correlated and that is allowed to have a
spectral density that is singular. A prediction error approach is followed in
which all node signals in the network are jointly predicted. The resulting
joint-direct identification method, generalizes the classical direct method for
closed-loop identification to handle situations of mutually correlated noise on
inputs and outputs. When applied to general dynamic networks with rank-reduced
noise, it appears that the natural identification criterion becomes a weighted
LS criterion that is subject to a constraint. This constrained criterion is
shown to lead to maximum likelihood estimates of the dynamic network and
therefore to minimum variance properties, reaching the Cramer-Rao lower bound
in the case of Gaussian noise.Comment: 17 pages, 5 figures, revision submitted for publication in
Automatica, 4 April 201
- …