000 04400nam a2200565 i 4500
001 5237512
003 IEEE
005 20220712205614.0
006 m o d
007 cr |n|||||||||
008 151221s2005 nyua ob 001 eng d
020 _a9780471723134
_qebook
020 _z9780470640371
_qprint
020 _z0780360001
_qprint ed.
020 _z9780780360006
_qprint ed.
020 _z0471723134
_qelectronic
024 7 _a10.1002/0471723134
_2doi
035 _a(CaBNVSL)mat05237512
035 _a(IDAMS)0b0000648109588c
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aQA402
_b.P56 2001eb
082 0 0 _a003/.85
_221
100 1 _aPintelon, R.,
_q(Rik)
_eauthor.
_926460
245 1 0 _aSystem identification :
_ba frequency domain approach /
_cRik Pintelon, Johan Schoukens.
264 1 _aNew York :
_bIEEE Press,
_cc2001.
264 2 _a[Piscataqay, New Jersey] :
_bIEEE Xplore,
_c[2005]
300 _a1 PDF (xxxviii, 605 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
505 0 _aPreface. Acknowledgments. List of Operators and National Conventions. List of Symbols. List of Abbreviations. An Introduction to Identification. Measurements of Frequency Response Functions. Frequency Response Function Measurements in the Presence of Nonlinear Distortions. Design of Excitation Signals. Models of Linear Time-Invariant Systems. An Intuitive Introduction to Frequency Domain Identification. Estimation with Known Noise Model. Estimation with Unknown Noise Model. Model Selection and Validation. Base Choices in System Identification. Guidelines for the User. Applications. Some Linear Algebra Fundamentals. Some Probability and Stochastic Convergence Fundamentals. Properties of Least Squares Estimators with Deterministic Weighting. Properties of Least Squares Estimators with Stochastic Weighting. Identification of Semilinear Models. Identification of Invariant of (Over) Parameterized Models. References. Subject Index. Reference Index. About the Authors.
506 1 _aRestricted to subscribers or individual electronic text purchasers.
520 _aElectrical Engineering System Identification A Frequency Domain Approach How does one model a linear dynamic system from noisy data? This book presents a general approach to this problem, with both practical examples and theoretical discussions that give the reader a sound understanding of the subject and of the pitfalls that might occur on the road from raw data to validated model. The emphasis is on robust methods that can be used with a minimum of user interaction. Readers in many fields of engineering will gain knowledge about: * Choice of experimental setup and experiment design * Automatic characterization of disturbing noise * Generation of a good plant model * Detection, qualification, and quantification of nonlinear distortions * Identification of continuous- and discrete-time models * Improved model validation tools and from the theoretical side about: * System identification * Interrelations between time- and frequency-domain approaches * Stochastic properties of the estimators * Stochastic analysis System Identification: A Frequency Domain Approach is written for practicing engineers and scientists who do not want to delve into mathematical details of proofs. Also, it is written for researchers who wish to learn more about the theoretical aspects of the proofs. Several of the introductory chapters are suitable for undergraduates. Each chapter begins with an abstract and ends with exercises, and examples are given throughout.
530 _aAlso available in print.
538 _aMode of access: World Wide Web
588 _aDescription based on PDF viewed 12/21/2015.
650 0 _aSystem identification.
_94634
653 _aControl Systems Technology.
655 0 _aElectronic books.
_93294
695 _aBibliographies
695 _aBiographies
695 _aFrequency domain analysis
695 _aIndexes
700 1 _aSchoukens, J.
_q(Johan)
_926461
710 2 _aJohn Wiley & Sons,
_epublisher.
_96902
710 2 _aIEEE Xplore (Online service),
_edistributor.
_926462
776 0 8 _iPrint version:
_z9780470640371
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5237512
942 _cEBK
999 _c73779
_d73779