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Model structure learning: A support vector machine approach for LPV linear-regression models.

, , , and . CDC-ECE, page 3192-3197. IEEE, (2011)

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Identification of LPV partial differential equation models., , , and . CDC, page 4547-4552. IEEE, (2013)Instrumental variable scheme for closed-loop LPV model identification., , , and . Automatica, 48 (9): 2314-2320 (2012)Wiener system identification by weighted principal component analysis., and . ECC, page 1705-1710. IEEE, (2014)An RKHS approach to systematic kernel selection in nonlinear system identification., , and . CDC, page 3898-3903. IEEE, (2016)Shrinking complexity of scheduling dependencies in LS-SVM based LPV system identification., , , and . CDC, page 2561-2566. IEEE, (2014)Model structure learning: A support vector machine approach for LPV linear-regression models., , , and . CDC-ECE, page 3192-3197. IEEE, (2011)Introducing instrumental variables in the LS-SVM based identification framework., , and . CDC-ECE, page 3198-3203. IEEE, (2011)Refined instrumental variable methods for identification of LPV Box-Jenkins models., , , and . Automatica, 46 (6): 959-967 (2010)LPV system identification under noise corrupted scheduling and output signal observations., , , and . Automatica, (2015)Single Minimum Nonlinearity Wiener System Identification by Weighted Principal Component Analysis., and . ALCOSP, page 384-389. International Federation of Automatic Control, (2013)