
Georgios BirpoutsoukisCatholic University of Louvain | UCLouvain · Institute of Information and Communication Technologies, Electronics and Applied Mathematics
Georgios Birpoutsoukis
Doctor of Engineering
Model Validator at KBC Bank & Verzekering
About
14
Publications
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Introduction
Publications
Publications (14)
For dynamic systems, the steady-state system response to periodic excitation is well understood for both linear and certain nonlinear system classes. When the excitation is not periodic, however, the measured response will contain both transient and steady-state contributions. For linear systems, these transient contributions have been thoroughly e...
Bayesian learning techniques have recently garnered significant attention in the system identification community. Originally introduced for low variance estimation of linear impulse response models, the concept has since been extended to the nonlinear setting for Volterra series estimation in the time domain. In this paper, we approach the estimati...
In this paper, the regularization approach introduced recently for nonparametric estimation of linear systems is extended to the estimation of nonlinear systems modelled as Volterra series. The kernels of order higher than one, representing higher dimensional impulse responses in the series, are considered to be realizations of multidimensional Gau...
This paper presents an efficient nonparametric time domain nonlinear system identification method. It is shown how truncated Volterra series models can be efficiently estimated without the need of long, transient-free measurements. The method is a novel extension of the regularization methods that have been developed for impulse response estimates...
A simple nonlinear system modeling algorithm designed to work with limited \emph{a priori }knowledge and short data records, is examined. It creates an empirical Volterra series-based model of a system using an $l_{q}$-constrained least squares algorithm with $q\geq 1$. If the system $m\left( \cdot \right) $ is a continuous and bounded map with a f...
Design of optimal input excitations is one of the most challenging problems in the field of system identification. The main difficulty lies in the fact that the optimization problem cannot always be formulated to be convex, therefore a globally optimal excitation for the dynamic system of interest cannot be guaranteed. In this paper, optimal input...
Kernel-based modeling of dynamic systems has garnered a significant amount of attention in the system identification literature since its introduction to the field. While the method was originally applied to linear impulse response estimation in the time domain, the concepts have since been extended to the frequency domain for estimation of frequen...
This paper presents an efficient nonparametric time domain nonlinear system identification method. It is shown how truncated Volterra series models can be efficiently estimated without the need of long, transient-free measurements. The method is a novel extension of the regularization methods that have been developed for impulse response estimates...
Cortical damage after a stroke often affects movement control, resulting in impairments such as paresis and synergies. Although some recover, most stroke survivors are left with reduced function of the upper limb, which has a severe impact on their activities of daily living. People who have suffered a stroke demonstrate heterogeneous impairments d...
This paper presents an efficient nonparametric time domain nonlinear system identification method applied to the measurement benchmark data of the cascaded water tanks. In this work a method to estimate efficiently finite Volterra kernels without the need of long records is presented. This work is a novel extension of the regularization methods tha...
A simple non-linear system modelling algorithm designed to work with limited a priori knowledge and short data records, is examined. It creates an empirical Volterra series-based model of a system using an lq-constrained least squares algorithm with q ≥ 1. If the system m· is a continuous and bounded map with a finite memory no longer than some kno...
In this paper, the regularization approach introduced recently for nonparametric estimation of linear systems is extended to the estimation of nonlinear systems modeled as Volterra series. The kernels of order higher than one, representing higher dimensional impulse responses in the series, are considered to be realizations of multidimensional Gaus...
Two batch-to-batch model update strategies for model-based control of batch cooling crystallization are presented. In Iterative Learning Control, a nominal process model is adjusted by a non-parametric, additive correction term which depends on the difference between the measured output and the model prediction in the previous batch. In Iterative I...
Modeling of nonlinear dynamic systems constitutes one of the most challenging topics in the field of system identifi- cation. One way to describe the nonlinear behavior of a process is by use of the nonparametric Volterra Series representation. The drawback of this method lies in the fact that the number of parameters to be estimated increases fast...