000			01673naaaa2200301uu 4500
001			https://directory.doabooks.org/handle/20.500.12854/54766
005			20220219201038.0
020			_aKSP/1000085419
020			_a9783731508342
024	7		_a10.5445/KSP/1000085419 _cdoi
041	0		_aEnglish
042			_adc
100	1		_aKenderi, Gábor _4auth
245	1	0	_aNonparametric identification of nonlinear dynamic systems
260			_bKIT Scientific Publishing _c2018
300			_a1 electronic resource (XXVIII, 194 p. p.)
506	0		_aOpen Access _2star _fUnrestricted online access
520			_aA nonparametric identification method for highly nonlinear systems is presented that is able to reconstruct the underlying nonlinearities without a priori knowledge of the describing nonlinear functions. The approach is based on nonlinear Kalman Filter algorithms using the well-known state augmentation technique that turns the filter into a dual state and parameter estimator, of which an extension towards nonparametric identification is proposed in the present work.
540			_aCreative Commons _fhttps://creativecommons.org/licenses/by-sa/4.0/ _2cc _4https://creativecommons.org/licenses/by-sa/4.0/
546			_aEnglish
653			_anichtlineare dynamische System
653			_aKalman Filter
653			_anonlinear dynamic system
653			_anonparametric identification
653			_anichtparametrische Identifikation
856	4	0	_awww.oapen.org _uhttps://www.ksp.kit.edu/9783731508342 _70 _zDOAB: download the publication
856	4	0	_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/54766 _70 _zDOAB: description of the publication
999			_c40844 _d40844