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We consider the problem of nonlinear system identification when prior knowledge is available on the region of attraction (ROA) of an equilibrium point.
L. Ljung,
1999
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I. Goethals, T. Van Gestel, J. Suykens, P. Van Dooren, and B. De Moor, “Identification of positive real models in subspace identification by using regularization,”
2003
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C. Carmeli, E. De Vito, and A. Toigo, “Vector–valued reproducing kernel Hilbert spaces of integrable functions and Mercer theorem,”
2006
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D.-X. Zhou, “Derivative reproducing properties for kernel methods in learning theory,”
2008
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S. Calinon, F. D’halluin, E. L. Sauser, D. G. Caldwell, and A. G. Billard, “Learning and reproduction of gestures by imitation,”
2010
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S. M. Khansari-Zadeh and A. Billard, “Learning stable nonlinear dynamical systems with Gaussian mixture models,”
2011
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A. J. Ijspeert, J. Nakanishi, H. Hoffmann, P. Pastor, and S. Schaal, “Dynamical movement primitives: learning attractor models for motor behaviors,”
2013
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G. Pillonetto, F. Dinuzzo, T. Chen, G. De Nicolao, and L. Ljung, “Kernel methods in system identification, machine learning and function estimation: A survey,”
2014
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——, “Learning control Lyapunov function to ensure stability of dynamical system-based robot reaching motions,”
2014
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M. Grant and S. Boyd, “CVX: Matlab software for disciplined convex programming, version 2.1,” 2014
2014
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J. Peypouquet,
2015
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2018
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J. Schoukens and L. Ljung, “Nonlinear system identification: A user-oriented road map,”
2019
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M. Khosravi and R. S. Smith, “Kernel-based identification of positive systems,”
2019
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2019
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W. Wang, P. Yu, L. Lin, and T. Tong, “Robust estimation of derivatives using locally weighted least absolute deviation regression,”
2019
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2017
Cited alongside, same era.