000			06287cam a2200733 i 4500
001			on1057238048
003			OCoLC
005			20220711203548.0
006			m o d
007			cr |||||||||||
008			181009t20192019njua ob 001 0 eng
010			_a 2018047949
040			_aDLC _beng _erda _epn _cDLC _dOCLCO _dOCLCF _dDG1 _dN$T _dYDX _dRECBK _dUKMGB _dEBLCP _dOCLCQ
015			_aGBB938058 _2bnb
016	7		_a019269532 _2Uk
019			_a1090426790 _a1090499652 _a1090543366 _a1090752829 _a1090762750
020			_a9781119457770 _q(electronic book)
020			_a1119457777 _q(electronic book)
020			_a9781119457787 _q(electronic publication)
020			_a1119457785 _q(electronic publication)
020			_a9781119457695 _q(electronic book)
020			_a1119457696 _q(electronic book)
020			_z9781119457763 _q(hardcover)
020			_z1119457769
029	1		_aAU@ _b000064319362
029	1		_aCHNEW _b001048766
029	1		_aCHVBK _b565571656
029	1		_aUKMGB _b019269532
035			_a(OCoLC)1057238048 _z(OCoLC)1090426790 _z(OCoLC)1090499652 _z(OCoLC)1090543366 _z(OCoLC)1090752829 _z(OCoLC)1090762750
037			_a9781119457787 _bWiley
042			_apcc
050	1	4	_aTK5102.9 _b.C3195 2019
072		7	_aTEC _x009070 _2bisacsh
082	0	0	_a621.382/23 _223
049			_aMAIN
100	1		_aCandy, James V., _eauthor. _98819
245	1	0	_aModel-based processing : _ban applied subspace identification approach / _cJames V. Candy, Lawrence Livermore National Laboratory, University of California Santa Barbara.
264		1	_aHoboken, NJ : _bJohn Wiley & Sons, Inc., _c2019.
264		4	_c©2019
300			_a1 online resource (xxv, 511 pages)
336			_atext _btxt _2rdacontent
337			_acomputer _bn _2rdamedia
338			_aonline resource _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
520			_a"Provides a model-based "bridge" for signal processors/control engineers enabling a coupling and motivation for model development and subsequent processor designs/applications - Incorporates an in-depth treatment of the subspace approach that applies a variety of the subspace algorithm to synthesized examples and actual applications - Introduces new, fast subspace identifiers, capable of developing the required model for processing/controls Market description: Primary audience: advanced seniors, 1st year graduate student (engineering, sciences) Secondary audience: engineering professionals"-- _cProvided by publisher.
588	0		_aOnline resource; title from digital title page (viewed on April 01, 2019).
505	0		_aCover; Title Page; Copyright; Contents; Preface; Acknowledgements; Glossary; Chapter 1 Introduction; 1.1 Background; 1.2 Signal Estimation; 1.3 Model-Based Processing; 1.4 Model-Based Identification; 1.5 Subspace Identification; 1.6 Notation and Terminology; 1.7 Summary; MATLAB Notes; References; Problems; Chapter 2 Random Signals and Systems; 2.1 Introduction; 2.2 Discrete Random Signals; 2.3 Spectral Representation of Random Signals; 2.4 Discrete Systems with Random Inputs; 2.4.1 Spectral Theorems; 2.4.2 ARMAX Modeling; 2.5 Spectral Estimation
505	8		_a2.5.1 Classical (Nonparametric) Spectral Estimation2.5.1.1 Correlation Method (Blackman-Tukey); 2.5.1.2 Average Periodogram Method (Welch); 2.5.2 Modern (Parametric) Spectral Estimation; 2.5.2.1 Autoregressive (All-Pole) Spectral Estimation; 2.5.2.2 Autoregressive Moving Average Spectral Estimation; 2.5.2.3 Minimum Variance Distortionless Response (MVDR) Spectral Estimation; 2.5.2.4 Multiple Signal Classification (MUSIC) Spectral Estimation; 2.6 Case Study: Spectral Estimation of Bandpass Sinusoids; 2.7 Summary; Matlab Notes; References; Problems
505	8		_aChapter 3 State-Space Models for Identification3.1 Introduction; 3.2 Continuous-Time State-Space Models; 3.3 Sampled-Data State-Space Models; 3.4 Discrete-Time State-Space Models; 3.4.1 Linear Discrete Time-Invariant Systems; 3.4.2 Discrete Systems Theory; 3.4.3 Equivalent Linear Systems; 3.4.4 Stable Linear Systems; 3.5 Gauss-Markov State-Space Models; 3.5.1 Discrete-Time Gauss-Markov Models; 3.6 Innovations Model; 3.7 State-Space Model Structures; 3.7.1 Time-Series Models; 3.7.2 State-Space and Time-Series Equivalence Models; 3.8 Nonlinear (Approximate) Gauss-Markov State-Space Models
505	8		_a3.9 SummaryMATLAB Notes; References; Chapter 4 Model-Based Processors; 4.1 Introduction; 4.2 Linear Model-Based Processor: Kalman Filter; 4.2.1 Innovations Approach; 4.2.2 Bayesian Approach; 4.2.3 Innovations Sequence; 4.2.4 Practical Linear Kalman Filter Design: Performance Analysis; 4.2.5 Steady-State Kalman Filter; 4.2.6 Kalman Filter/Wiener Filter Equivalence; 4.3 Nonlinear State-Space Model-Based Processors; 4.3.1 Nonlinear Model-Based Processor: Linearized Kalman Filter; 4.3.2 Nonlinear Model-Based Processor: Extended Kalman Filter
505	8		_a4.3.3 Nonlinear Model-Based Processor: Iterated-Extended Kalman Filter4.3.4 Nonlinear Model-Based Processor: Unscented Kalman Filter; 4.3.5 Practical Nonlinear Model-Based Processor Design: Performance Analysis; 4.3.6 Nonlinear Model-Based Processor: Particle Filter; 4.3.7 Practical Bayesian Model-Based Design: Performance Analysis; 4.4 Case Study: 2D-Tracking Problem; 4.5 Summary; MATLAB Notes; References; Problems; Chapter 5 Parametrically Adaptive Processors; 5.1 Introduction; 5.2 Parametrically Adaptive Processors: Bayesian Approach
650		0	_aSignal processing _xDigital techniques _xMathematics. _97229
650		0	_aAutomatic control _xMathematical models. _97898
650		0	_aInvariant subspaces. _98820
650		7	_aTECHNOLOGY & ENGINEERING _xMechanical. _2bisacsh _98821
650		7	_aAutomatic control _xMathematical models. _2fast _0(OCoLC)fst00822712 _97898
650		7	_aInvariant subspaces. _2fast _0(OCoLC)fst00977981 _98820
650		7	_aSignal processing _xDigital techniques _xMathematics. _2fast _0(OCoLC)fst01118294 _97229
655		4	_aElectronic books. _93294
776	0	8	_iPrint version: _aCandy, James V. _tModel-based processing. _dHoboken, NJ : John Wiley & Sons, Inc., [2018] _z9781119457763 _w(DLC) 2018044855
856	4	0	_uhttps://doi.org/10.1002/9781119457695 _zWiley Online Library
942			_cEBK
994			_aC0 _bDG1
999			_c69209 _d69209