Fetching the paper…
Reading the bibliography…
With a specific emphasis on control design objectives, achieving accurate system modeling with limited complexity is crucial in parametric system identification.
Principal component analysis in linear systems: Controllability, observability, and model reduction
B. Moore · 1981
Earlier work this paper cites.
All optimal hankel-norm approximations of linear multivariable systems and their L ∞ L^{\infty} -error bounds
K. Glover · 1984
Earlier work this paper cites.
Model reduction via balanced realizations: an extension and frequency weighting techniques
U.M. Al-Saggaf and G. F. Franklin · 1988
Earlier work this paper cites.
Singular perturbation approximation of balanced systems
Y. Liu and B. O. D. Anderson · 1989
Earlier work this paper cites.
Prefix sums and their applications
G. E. Blelloch · 1990
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
R. Tibshirani · 1996
Earlier work this paper cites.
A rank minimization heuristic with application to minimum order system approximation
M. Fazel, H. Hindi, and S.P. Boyd · 2001
Earlier work this paper cites.
Approximation of Large-Scale Dynamical Systems
A. C. Antoulas · 2005
Earlier work this paper cites.
Subspace Methods for System Identification
T. Katayama · 2005
Earlier work this paper cites.
Linear Robust Control
M. Green and D. Limebeer · 2012
Cited alongside, same era.
Regularized linear system identification using atomic, nuclear and kernel-based norms: The role of the stability constraint
G. Pillonetto, T. Chen, A. Chiuso, G. De Nicolao, and L. Ljung · 2016
Cited alongside, same era.
Language modeling with gated convolutional networks
Y. N. Dauphin, A. Fan, M. Auli, and D. Grangier · 2017
Cited alongside, same era.
F-16 aircraft benchmark based on ground vibration test data
J. P. Noël and M. Schoukens · 2017
Cited alongside, same era.
Identification of block-oriented nonlinear systems starting from linear approximations: A survey
M. Schoukens and K. Tiels · 2017
Cited alongside, same era.
Learning neural state-space models: do we need a state estimator?
M. Forgione, M. Mejari, and D. Piga · 2022
Later among the works it cites.
On the parameterization and initialization of diagonal state space models
A. Gu, K. Goel, A. Gupta, and C. Ré · 2022
Later among the works it cites.
Efficiently modeling long sequences with structured state spaces
A. Gu, K. Goel, and C. Ré · 2022
Later among the works it cites.
Simplified state space layers for sequence modeling
J. TH. Smith, A. Warrington, and S. W. Linderman · 2022
Later among the works it cites.
Resurrecting recurrent neural networks for long sequences
A. Orvieto, S. L. Smith, A. Gu, A. Fernando, C. Gulcehre, R. Pascanu, and S. De · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Wu and K. He · 2018
Cited alongside, same era.
Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al · 2019
Cited alongside, same era.
Nonlinear system identification: A user-oriented road map
J. Schoukens and L. Ljung · 2019
Cited alongside, same era.
Long range arena: A benchmark for efficient transformers
Y. Tay, M. Dehghani, S. Abnar, D. Shen, Y.and Bahri, P. Pham, J. Rao, L. Yang, S. Ruder, and D. Metzler · 2020
Cited alongside, same era.
Recurrent equilibrium networks: Flexible dynamic models with guaranteed stability and robustness
M. Revay, R. Wang, and I. R. Manchester · 2023
Later among the works it cites.
A. Bemporad · 2024
Closest in time.
Interconnection-based model order reduction - a survey
G. Scarciotti and A. Astolfi · 2024
Closest in time.