000 04372nam a22005895i 4500
001 978-0-8176-4956-2
003 DE-He213
005 20140220084457.0
007 cr nn 008mamaa
008 100528s2010 xxu| s |||| 0|eng d
020 _a9780817649562
_9978-0-8176-4956-2
024 7 _a10.1007/978-0-8176-4956-2
_2doi
050 4 _aQ295
050 4 _aQA402.3-402.37
072 7 _aGPFC
_2bicssc
072 7 _aSCI064000
_2bisacsh
072 7 _aTEC004000
_2bisacsh
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
912 _aZDB-2-SMA
999 _c109924
_d109924