000			04308nam a22005535i 4500
001			978-0-8176-4956-2
003			DE-He213
005			20251006084440.0
007			cr nn 008mamaa
008			100528s2010 xxu| s |||| 0|eng d
020			_a9780817649562
020			_a99780817649562
024	7		_a10.1007/978-0-8176-4956-2 _2doi
082	0	4	_a519 _223
100	1		_aWang, Le Yi. _eauthor.
245	1	0	_aSystem Identification with Quantized Observations _h[electronic resource] / _cby Le Yi Wang, G. George Yin, Ji-Feng Zhang, Yanlong Zhao.
264		1	_aBoston : _bBirkhäuser Boston, _c2010.
300			_aXVIII, 317p. 42 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSystems & Control: Foundations & Applications
505	0		_aOverview -- System Settings -- Stochastic Methods for Linear Systems -- Empirical-Measure-Based Identification: Binary-Valued Observations -- Estimation Error Bounds: Including Unmodeled Dynamics -- Rational Systems -- Quantized Identification and Asymptotic Efficiency -- Input Design for Identification in Connected Systems -- Identification of Sensor Thresholds and Noise Distribution Functions -- Deterministic Methods for Linear Systems -- Worst-Case Identification under Binary-Valued Observations -- Worst-Case Identification Using Quantized Observations -- Identification of Nonlinear and Switching Systems -- Identification of Wiener Systems with Binary-Valued Observations -- Identification of Hammerstein Systems with Quantized Observations -- Systems with Markovian Parameters -- Complexity Analysis -- Space and Time Complexities, Threshold Selection, Adaptation -- Impact of Communication Channels on System Identification.
520			_aThis book presents recently developed methodologies that utilize quantized information in system identification and explores their potential in extending control capabilities for systems with limited sensor information or networked systems. The results of these methodologies can be applied to signal processing and control design of communication and computer networks, sensor networks, mobile agents, coordinated data fusion, remote sensing, telemedicine, and other fields in which noise-corrupted quantized data need to be processed. Providing a comprehensive coverage of quantized identification, the book treats linear and nonlinear systems, as well as time-invariant and time-varying systems. The authors examine independent and dependent noises, stochastic- and deterministic-bounded noises, and also noises with unknown distribution functions. The key methodologies combine empirical measures and information-theoretic approaches to derive identification algorithms, provide convergence and convergence speed, establish efficiency of estimation, and explore input design, threshold selection and adaptation, and complexity analysis. System Identification with Quantized Observations is an excellent resource for graduate students, systems theorists, control engineers, applied mathematicians, as well as practitioners who use identification algorithms in their work. Selected material from the book may be used in graduate-level courses on system identification.
650		0	_aMATHEMATICS.
650		0	_aSYSTEMS THEORY.
650		0	_aALGORITHMS.
650		0	_aDISTRIBUTION (PROBABILITY THEORY).
650		0	_aTELECOMMUNICATION.
650	1	4	_aMATHEMATICS.
650	2	4	_aSYSTEMS THEORY, CONTROL.
650	2	4	_aCONTROL.
650	2	4	_aALGORITHMS.
650	2	4	_aCOMMUNICATIONS ENGINEERING, NETWORKS.
650	2	4	_aPROBABILITY THEORY AND STOCHASTIC PROCESSES.
650	2	4	_aSIGNAL, IMAGE AND SPEECH PROCESSING.
700	1		_aYin, G. George. _eauthor.
700	1		_aZhang, Ji-Feng. _eauthor.
700	1		_aZhao, Yanlong. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780817649555
830		0	_aSystems & Control: Foundations & Applications
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-8176-4956-2 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c59832 _d59832