Zobrazeno 1 - 10
of 696
pro vyhledávání: '"Ljung, Lennart"'
Publikováno v:
IFAC-PapersOnLine, July 2024, 20th IFAC Symposium on System Identification SYSID 2024
MATLAB(R) releases over the last 3 years have witnessed a continuing growth in the dynamic modeling capabilities offered by the System Identification Toolbox(TM). The emphasis has been on integrating deep learning architectures and training technique
Externí odkaz:
http://arxiv.org/abs/2409.07642
Autor:
Pillonetto, Gianluigi, Chen, Tianshi, Chiuso, Alessandro, De Nicolao, Giuseppe, Ljung, Lennart
This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel
Externí odkaz:
https://library.oapen.org/handle/20.500.12657/56998
Autor:
Pillonetto, Gianluigi, Aravkin, Aleksandr, Gedon, Daniel, Ljung, Lennart, Ribeiro, Antônio H., Schön, Thomas B.
Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results in many application areas that reveal essential but hidden properties o
Externí odkaz:
http://arxiv.org/abs/2301.12832
Regularized system identification is the major advance in system identification in the last decade. Although many promising results have been achieved, it is far from complete and there are still many key problems to be solved. One of them is the asy
Externí odkaz:
http://arxiv.org/abs/2209.12231
Autor:
Ohlsson, Henrik, Ljung, Lennart
This paper proposes a general convex framework for the identification of switched linear systems. The proposed framework uses over-parameterization to avoid solving the otherwise combinatorially forbidding identification problem, and takes the form o
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-92612
In this paper, we give a tutorial on asymptotic properties of the Least Square (LS) and Regularized Least Squares (RLS) estimators for the finite impulse response model with filtered white noise inputs. We provide three perspectives: the almost sure
Externí odkaz:
http://arxiv.org/abs/2112.10319
Publikováno v:
In Automatica February 2024 160
The asymptotic optimality (a.o.) of various hyper-parameter estimators with different optimality criteria has been studied in the literature for regularized least squares regression problems. The estimators include e.g., the maximum (marginal) likeli
Externí odkaz:
http://arxiv.org/abs/2104.10471
Autor:
Yaghmaie, Farnaz Adib, Ljung, Lennart
The emerging field of Reinforcement Learning (RL) has led to impressive results in varied domains like strategy games, robotics, etc. This handout aims to give a simple introduction to RL from control perspective and discuss three possible approaches
Externí odkaz:
http://arxiv.org/abs/2103.04910
Deep state space models (SSMs) are an actively researched model class for temporal models developed in the deep learning community which have a close connection to classic SSMs. The use of deep SSMs as a black-box identification model can describe a
Externí odkaz:
http://arxiv.org/abs/2003.14162