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001 | 978-981-99-0593-5 | ||
003 | DE-He213 | ||
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008 | 230320s2023 si | s |||| 0|eng d | ||
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_a9789819905935 _9978-981-99-0593-5 |
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_a515.39 _223 |
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_aHuang, Ke. _eauthor. _0(orcid)0000-0002-3625-3937 _1https://orcid.org/0000-0002-3625-3937 _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aBayesian Real-Time System Identification _h[electronic resource] : _bFrom Centralized to Distributed Approach / _cby Ke Huang, Ka-Veng Yuen. |
250 | _a1st ed. 2023. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2023. |
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300 |
_aXII, 276 p. 154 illus., 127 illus. in color. _bonline resource. |
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_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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500 | _aAcceso multiusuario | ||
505 | 0 | _aChapter 1. Introduction -- Chapter 2. System identification by Kalman filter and extended Kalman filter -- Chapter 3. Outlier detection for real-time system identification -- Chapter 4. Real-time updating of noise parameters for structural identification -- Chapter 5. Bayesian model class selection for real-time system identification -- Chapter 6. Online distributed identification for wireless sensor networks -- Chapter 7. Online distributed identification handling asynchronous data and multiple outlier-corrupted data. | |
520 | _aThis book introduces some recent developments in Bayesian real-time system identification. It contains two different perspectives on data processing for system identification, namely centralized and distributed. A centralized Bayesian identification framework is presented to address challenging problems of real-time parameter estimation, which covers outlier detection, system, and noise parameters tracking. Besides, real-time Bayesian model class selection is introduced to tackle model misspecification problem. On the other hand, a distributed Bayesian identification framework is presented to handle asynchronous data and multiple outlier corrupted data. This book provides sufficient background to follow Bayesian methods for solving real-time system identification problems in civil and other engineering disciplines. The illustrative examples allow the readers to quickly understand the algorithms and associated applications. This book is intended for graduate students and researchers in civil and mechanical engineering. Practitioners can also find useful reference guide for solving engineering problems. | ||
541 |
_fUABC ; _cPerpetuidad |
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650 | 0 | _aDynamics. | |
650 | 0 | _aNonlinear theories. | |
650 | 0 | _aStatistics . | |
650 | 0 | _aCivil engineering. | |
650 | 1 | 4 | _aApplied Dynamical Systems. |
650 | 2 | 4 | _aBayesian Inference. |
650 | 2 | 4 | _aCivil Engineering. |
700 | 1 |
_aYuen, Ka-Veng. _eauthor. _0(orcid)0000-0002-1755-6668 _1https://orcid.org/0000-0002-1755-6668 _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789819905928 |
776 | 0 | 8 |
_iPrinted edition: _z9789819905942 |
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_iPrinted edition: _z9789819905959 |
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_zLibro electrónico _uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-99-0593-5 |
912 | _aZDB-2-ENG | ||
912 | _aZDB-2-SXE | ||
942 | _cLIBRO_ELEC | ||
999 |
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