000			04274nam a2200397 i 4500
999			_c102134 _d102134 _x1
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007			cr nn 008mamaa
008			180407s2018 gw | fo |||| 0|eng d
020			_a9783319750019
024	7		_a10.1007/978-3-319-75001-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
041	0		_aeng
050		4	_aQA275 _b2018 EB
100	1		_aSöderström, Torsten _eautor _9673045
245	1	0	_aErrors-in-Variables Methods in System Identification _cby Torsten Söderström
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XXVII, 485 páginas) _b30 ilustraciones, 3 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aCommunications and Control Engineering, _x0178-5354
490	0		_aEngineering (Springer-11647)
505	0		_aChapter 1. Introduction -- Chapter 2. The Static Case -- Chapter 3. The Errors-in-Variables Problem for Dynamic Systems -- Chapter 4. Identifiability Aspects -- Chapter 5. Modeling Aspects -- Chapter 6. Elementary Methods -- Chapter 7. Methods Based on Bias-Compensation -- Chapter 8. Covariance Matching -- Chapter 9. Prediction Error and Maximum Likelihood Methods -- Chapter 10. Frequency Domain Methods -- Chapter 11. Total Least Squares -- Chapter 12. Methods for Periodic Data -- Chapter 13. Algorithmic Properties -- Chapter 14. Asymptotic Distributions -- Chapter 15. Errors-in-Variables Problems in Practice -- Index -- References.
520	3		_aThis book presents an overview of the different errors-in-variables (EIV) methods that can be used for system identification. Readers will explore the properties of an EIV problem. Such problems play an important role when the purpose is the determination of the physical laws that describe the process, rather than the prediction or control of its future behaviour. EIV problems typically occur when the purpose of the modelling is to get physical insight into a process. Identifiability of the model parameters for EIV problems is a non-trivial issue, and sufficient conditions for identifiability are given. The author covers various modelling aspects which, taken together, can find a solution, including the characterization of noise properties, extension to multivariable systems, and continuous-time models. The book finds solutions that are constituted of methods that are compatible with a set of noisy data, which traditional approaches to solutions, such as (total) least squares, do not find. A number of identification methods for the EIV problem are presented. Each method is accompanied with a detailed analysis based on statistical theory, and the relationship between the different methods is explained. A multitude of methods are covered, including: instrumental variables methods; methods based on bias-compensation; covariance matching methods; and prediction error and maximum-likelihood methods. The book shows how many of the methods can be applied in either the time or the frequency domain and provides special methods adapted to the case of periodic excitation. It concludes with a chapter specifically devoted to practical aspects and user perspectives that will facilitate the transfer of the theoretical material to application in real systems. Errors-in-Variables Methods in System Identification gives readers the possibility of recovering true system dynamics from noisy measurements, while solving over-determined systems of equations, making it suitable for statisticians and mathematicians alike. The book also acts as a reference for researchers and computer engineers because of its detailed exploration of EIV problems. .
988			_aEBSPRINGER_2018
650		7	_2embne _9673046 _aAnálisis de errores
776	0	8	_iEdición impresa: _z9783319750002
776	0	8	_iEdición impresa: _z9783319750026
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-75001-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2020 _dz _eb _zSI