ERNSI WS
System Identification
2008
The seventeenth ERNSI Workshop in System Identification will be held October 1-3 2008 in Sigtuna at the Sigtuna Foundation,
http://www.sigtunastiftelsen.se/english.asp/id/60
Sigtuna is
very close to the Stockholm Arlanda Airport (20 minutes by taxi), and is a small
old cultural city. The conference place is also very special and in line with
ERNSI traditions.
The
workshop is sponsored by the VR Linnaeus Research Center ACCESS (Autonomic
Complex Communication nEtworks Signals and Systems) at KTH,
www.access.ee.kth.se,
the Center for Industrial and Applied Mathematics, CIAM at KTH and Uppsala
University.
Final Program
Wednesday October 1
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11:00 - 12:00 |
Registration |
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12:00 - 13:00 |
Lunch |
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13:00 - 13:15 |
Welcome |
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13:15 - 14:00 |
Fredrik Gustafsson invited speaker (ULIN) |
Modeling for Kalman filtering
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14:00 - 14.30 |
Johan Schoukens, Kurt Barbé, Laurent Vanbeylen and
Rik Pintelon (VUB) |
Study of the nonlinear induced variability in linear
system identification |
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14:30 - 15:00 |
Marco Campi (UNIBS) |
Interval prediction models: Identification and
reliability |
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15:00 - 16:30 |
Coffee & Poster session |
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Poster 1 |
Cristian Rojas (KTH) |
Fundamental limitations in system identification and their application
to experiment design |
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Poster 2 |
Kurt Barbé (VUB) |
Using ANOVA in a microwave Round-robin comparison |
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Poster 3 |
Agnes Rensfelt and Torsten Söderström (UPPSALA) |
Validation of wave propagation models used in
nonparametric identification of viscoelastic |
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Poster 4 |
John Lataire and Rik Pintelon (VUB) |
How fast is a time-varying system varying? |
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Poster 5 |
Rogier Blom, Paul Van
den Hof, Hans Langen and Rob Munnig Schmidt (TUD) |
Multivariable
frequency response function estimation of a micro-milling spindle with
active magnetic bearings |
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Poster 6 |
Eric Wernholt (ULIN) |
Frequency-domain identification of industrial robots |
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Poster 7 |
Jan-Willem van Wingerden and Michel Verhaegen
(TUD)
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The analogy between LPV and LTI predictor-based
subspace identification |
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Poster 8 |
Enrico Avventi and Per
Enqvist (KTH) |
Approximative linear and
logarithmic interpolation of spectra |
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16:30 - 18:30 |
White paper session chaired by Håkan Hjalmarsson
(KTH) |
A road map for system identification |
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19:30 |
Dinner |
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Thursday October 2
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08:15 - 08:45 |
Ben Hanzon (UCC) |
Subdiagonal pivot structures and associated canonical forms under state
isometries |
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08:45 - 09:15 |
Wolfgang Scherrer (TUW) |
Rational approximation of fractionally integrated
processes |
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09:15 - 09:45 |
Tzvetan Ivanov (UCL) |
Applications of real rational modules in system identification |
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09:45 - 11:15 |
Coffee & Poster session |
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Poster 1 |
Cristian Rojas, Märta Barenthin Syberg, James Welsh
and Håkan Hjalmarsson (KTH) |
The cost of complexity in system identification |
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Poster 2 |
Philippe Dreesen (KULEUVEN) |
The
Riemannian singular value decomposition in dynamic system identification |
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Poster 3 |
Laurent Vanbeylen, Rik Pintelon, Pieter de Groen
(VUB) |
Blind maximum likelihood identification of Wiener
systems with measurement noise |
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Poster 4 |
Torsten Söderström, Magnus Mossberg and Mei Hong
(UPPSALA) |
A covariance
matching approach for identifying errors-in-variables systems |
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Poster 5 |
Ludwig De Locht, Gerd Vandersteen and Yves Rolain |
Identification of the nonlinear circuit contributions
caused by multitone excitation |
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Poster 6 |
Märta Barenthin Syberg, Bo Wahlberg, Håkan
Hjalmarsson and Mathias Barkhagen (KTH) |
L2 gain estimation in noise |
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Poster 7 |
Bo Wahlberg (KTH) |
On identification of cascade systems |
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Poster 8 |
Roland Toth (TUD) |
A prediction-error framework for LPV systems |
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11:15 - 12:15 |
Timo Koski, invited speaker (KTH) |
On learning of structures for Bayesian networks |
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12:15 - 13.30 |
Lunch |
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13:30 - 14:00 |
Vincent Laurin,
Marion Gilson,
Hugues Garnier and Peter Young
(UHP-NANCY)
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An IV-based method for non-linear continuous-time Hammerstein model
identification: Application to rainfall-flow modelling
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14:00 - 14:30 |
László Gerencsér and Vilmos Prokaj (SZTAKI) |
Stability of hybrid linear stochastic
Systems
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14:30 - 15:00 |
Tillmann Falck
(KULEUVEN) |
Robust
kernel based regression in SOCP and LS formulations for perturbation
analysis |
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15:15 |
Bus to Uppsala |
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16:00 - 17:00 |
Visit Gustavianum |
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17:00 -18:00 |
Free time in Uppsala |
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18:15 |
Bus from Uppsala |
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19:30 |
Dinner |
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Friday October 3
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08:30 - 09:00 |
Christian Lyzell, Martin Enqvist and Lennart Ljung
(ULIN) |
An algebraic approach to convexification of system
identification problems
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09:00 - 09:30 |
Huseyin Akcay (UCL) |
Synthesis of
complete orthonormal fractional bases
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09:30 - 10:00 |
Gianluigi Pillonetto, Alessandro Chiuso and Giuseppe
De Nicolao (UNIPD)
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Predictor estimation via Gaussian regression: theory and applications |
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10:00 - 10:30 |
Coffee
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10:30 - 11:30 |
Peter
Grunwald, invited speaker (CWI) |
The catch-up phenomenon in model selection and prediction
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11:30 - 12:00 |
Thomas Schön (ULIN) |
A new algorithm for calibrating a combined camera and
IMU sensor unit
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12:00 - 13:15 |
Lunch
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13:15 - 13.45 |
Martin Enqvist (ULIN) |
An improved
weighting method for initialization of Hammerstein or Wiener system
identification algorithms
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13:45 - 14:15 |
Jonas
Sjöberg (VUB/CHALMERS), Per-Olof Gutman, Makul Agerwal, Mike Bax
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Tuning of a PID-controller for the Furuta pendulum
using a sequence of identifications of linearized time-varying models
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14:15 - 14:45 |
Henrik Ohlsson (ULIN) |
Manifold-constrained regressors in system
identification
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14:45 - 15:00 |
Closing
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Invited
Presentations
Fredrik Gustafsson, ULIN, Sweden
Modelling for Kalman filtering
Abstract:
Modeling for filtering is a rather unexplored area in
contrast to identification for control. Even if the true system is known, it is
not clear which linear model is best suited to use in a Kalman filter. In
contrast to control applications, the predictive ability of the model is more
important than the dynamics from the input. The presentation aims at maximizing
confusion by providing no solutions but only a number of practical and toy
examples, where the best model order is larger or smaller than the true system,
and where the input dynamics is not used.
Timo Koski, KTH, Sweden
On learning of
structures for Bayesian networks
Peter Grundwald, CWI, the Netherlands
The Catch-Up
Phenomenon in Model Selection and Prediction
Abstract:
Standard Bayesian model selection/averaging sometimes
learn too slowly: there exist other learning methods that lead to better
predictions based on less data. We give a novel analysis of this "catch-up"
phenomenon.
We
resolve a long-standing debate in statistics, known as the AIC-BIC dilemma:
model selection/averaging methods like BIC, Bayes, and MDL are consistent (they
eventually infer the correct model) but, when used for prediction, the rate at
which predictions improve can be suboptimal. Methods like AIC and leave-one-out
cross-validation are inconsistent but typically converge at the optimal rate.
Both AIC and BIC may be viewed as striking a trade-off between a model's
goodness-of-fit and its complexity, but under AIC, the 'complexity' of any given
model is typically smaller than under BIC.
We
give a novel analysis of the slow convergence of the BIC/Bayesian-type methods.
Based on this analysis, we propose the switching method, a modification of
Bayesian model averaging that achieves both consistency and minimax optimal
convergence rates. The method is related to expert-tracking algorithms developed
in the COLT literature, and has time complexity comparable to Bayes. Experiments
with nonparametric density estimation confirm that our large-sample theoretical
results also hold in practice in small samples.
Joint work with T. van Erven and S. de Rooij.