Zobrazeno 1 - 10
of 447
pro vyhledávání: '"Hjalmarsson, Håkan"'
Autor:
He, Jiabao, Hjalmarsson, Håkan
Subspace identification method (SIM) has been proven to be very useful and numerically robust for estimating state-space models. However, it is in general not believed to be as accurate as the prediction error method (PEM). Conversely, PEM, although
Externí odkaz:
http://arxiv.org/abs/2411.00506
Autor:
Hof, Paul M. J. Van den, Shi, Shengling, Fonken, Stefanie J. M., Ramaswamy, Karthik R., Hjalmarsson, Håkan, Dankers, Arne G.
When estimating models of of a multivariable dynamic system, a typical condition for consistency is to require the input signals to be persistently exciting, which is guaranteed if the input spectrum is positive definite for a sufficient number of fr
Externí odkaz:
http://arxiv.org/abs/2409.03883
Markov parameters play a key role in system identification. There exists many algorithms where these parameters are estimated using least-squares in a first, pre-processing, step, including subspace identification and multi-step least-squares algorit
Externí odkaz:
http://arxiv.org/abs/2405.04258
Subspace identification methods (SIMs) have proven very powerful for estimating linear state-space models. To overcome the deficiencies of classical SIMs, a significant number of algorithms has appeared over the last two decades, where most of them i
Externí odkaz:
http://arxiv.org/abs/2405.04250
Soft sensing is a way to indirectly obtain information of signals for which direct sensing is difficult or prohibitively expensive. It may not \textit{a priori} be evident which sensors provide useful information about the target signal, and various
Externí odkaz:
http://arxiv.org/abs/2405.03783
While subspace identification methods (SIMs) are appealing due to their simple parameterization for MIMO systems and robust numerical realizations, a comprehensive statistical analysis of SIMs remains an open problem, especially in the non-asymptotic
Externí odkaz:
http://arxiv.org/abs/2404.17331
We study the problem of determining an effective exploration strategy in static and non-linear optimization problems, which depend on an unknown scalar parameter to be learned from online collected noisy data. An optimal trade-off between exploration
Externí odkaz:
http://arxiv.org/abs/2403.15344
The maximum absolute correlation between regressors, which is called mutual coherence, plays an essential role in sparse estimation. A regressor matrix whose columns are highly correlated may result from optimal input design, since there is no constr
Externí odkaz:
http://arxiv.org/abs/2402.06048
In this paper, we consider the well known problem of non-linear identification of the rates of the reactions involved in cells with Monod functions. In bioprocesses, generating data is very expensive and long and so it is important to incorporate pri
Externí odkaz:
http://arxiv.org/abs/2402.04727
In this work, we consider the problem of regret minimization in adaptive minimum variance and linear quadratic control problems. Regret minimization has been extensively studied in the literature for both types of adaptive control problems. Most of t
Externí odkaz:
http://arxiv.org/abs/2211.07949