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In recent years, several algorithms for system identification with neural state-space models have been introduced.
Approximation by superpositions of a sigmoidal function
G. Cybenko · 1989
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Non-linear system identification using neural networks
S. Chen, S. A. Billings, and P. M. Grant · 1990
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Data-based hybrid modelling of the component placement process in pick-and-place machines
A.Lj Juloski, W.P.M.H Heemels, and G Ferrari-Trecate · 2004
Earlier work this paper cites.
Least squares support vector machines for kernel cca in nonlinear state-space identification
V. Verdult, J. A. K. Suykens, J. Boets, I. Goethals, and B. De Moor · 2004
Earlier work this paper cites.
A bounded-error approach to piecewise affine system identification
A. Bemporad, A. Garulli, S Paoletti, and A. Vicino · 2005
Earlier work this paper cites.
Optimal state estimation: Kalman, H infinity, and nonlinear approaches
D. Simon · 2006
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The elements of statistical learning: data mining, inference, and prediction
Trevor Hastie, Robert Tibshirani, Jerome H Friedman, and Jerome H Friedman · 2009
Earlier work this paper cites.
Wiener-Hammerstein benchmark
L. Ljung, J. Schoukens, and J. Suykens · 2009
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Deep learning in neural networks: An overview
J. Schmidhuber · 2015
Cited alongside, same era.
Predictive control for linear and hybrid systems
F. Borrelli, A. Bemporad, and M. Morari · 2017
Cited alongside, same era.
Design and analysis of experiments
D. C. Montgomery · 2017
Cited alongside, same era.
Deep learning for universal linear embeddings of nonlinear dynamics
B. Lusch, J. N. Kutz, and S. Brunton · 2018
Cited alongside, same era.
Learning nonlinear state-space models using deep autoencoders
D. Masti and A. Bemporad · 2018
Cited alongside, same era.
Recursive nonlinear-system identification using latent variables
P. Mattsson, D. Zachariah, and P. Stoica · 2018
Cited alongside, same era.
Identification of hybrid and linear parameter‐varying models via piecewise affine regression using mixed integer programming
M. Mejari, V.V. Naik, D. Piga, and A. Bemporad · 2020
Later among the works it cites.
Rao-Blackwellized sampling for batch and recursive Bayesian inference of Piecewise Affine models
D. Piga, A. Bemporad, and A. Benavoli · 2020
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On the smoothness of nonlinear system identification
A. H. Ribeiro, K. Tiels, J. Umenberger, T. B. Schön, and L. A. Aguirre · 2020
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Nonlinear state-space identification using deep encoder networks
G. Beintema, R. Tóth, and M. Schoukens · 2021
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dynoNet : A neural network architecture for learning dynamical systems
M. Forgione and D. Piga · 2021
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Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks
U. Michelucci · 2018
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
Cited alongside, same era.
Model structures and fitting criteria for system identification with neural networks
M. Forgione and D. Piga · 2020
Cited alongside, same era.
L. Iacob, G. Beintema, M. Schoukens, and R. Tóth · 2021
Later among the works it cites.
Learning nonlinear state–space models using autoencoders
D. Masti and A. Bemporad · 2021
Later among the works it cites.
Kernel-based system identification with manifold regularization: A bayesian perspective
M. Mazzoleni, A. Chiuso, M. Scandella, S. Formentin, and F. Previdi · 2022
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