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001			u376402
003			SIRSI
005			20160812084411.0
007			cr nn 008mamaa
008			110802s2011 gw | s |||| 0|eng d
020			_a9783642219818 _9978-3-642-21981-8
040			_cMX-MeUAM
050		4	_aTJ212-225
082	0	4	_a629.8 _223
100	1		_aYoung, Peter C. _eauthor.
245	1	0	_aRecursive Estimation and Time-Series Analysis _h[recurso electrónico] : _bAn Introduction for the Student and Practitioner / _cby Peter C. Young.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_aXVIII, 502p. 132 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aIntroduction -- Part I Recursive Estimation of Parameters in Linear Regression Models -- Recursive Estimation: A Simple Tutorial Introduction -- Recursive Least Squares Estimation -- Recursive Estimation of Time Variable Parameters in Regression Models -- Unobserved Component Models -- Part II Recursive Estimation of Parameters in Transfer Function Models -- Transfer Function Models and the Limitations of Recursive Least Squares -- Optimal Identification and Estimation of Discrete-Time Transfer Function Models -- Optimal Identification and Estimarization of Continuous-Time Transfer Function Models -- Identification of TF models in Closed-Loop -- Real-Time Recursive Parameter Estimation -- Part III Other Topics -- State-Dependent Parameter Estimation -- Data-Based Mechanistic (DBM) modeling.
520			_aThis is a revised version of  the 1984 book of the same name but considerably modified and enlarged to accommodate the developments in recursive estimation and time series analysis that have occurred over the last quarter century. Also over this time, the CAPTAIN Toolbox for recursive estimation and time series analysis has been developed by my colleagues and I at Lancaster, for use in the MatlabTM software environment (see Appendix G). Consequently, the present version of the book is able to exploit the many computational routines that are contained in this widely available Toolbox, as well as some of the other routines in MatlabTM and its other toolboxes. The book is an introductory one on the topic of recursive estimation and it demonstrates how this approach to estimation, in its various forms, can be an impressive aid to the modelling of stochastic, dynamic systems. It is intended for undergraduate or Masters students who wish to obtain a grounding in this subject; or for practitioners in industry who may have heard of topics dealt with in this book and, while they want to know more about them, may have been deterred by the rather esoteric nature of some books in this challenging area of study.
650		0	_aEngineering.
650		0	_aDistribution (Probability theory).
650		0	_aVibration.
650		0	_aTelecommunication.
650	1	4	_aEngineering.
650	2	4	_aControl.
650	2	4	_aProbability Theory and Stochastic Processes.
650	2	4	_aComplex Networks.
650	2	4	_aCommunications Engineering, Networks.
650	2	4	_aVibration, Dynamical Systems, Control.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642219801
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-21981-8
596			_a19
942			_cLIBRO_ELEC
999			_c204282 _d204282