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Model-based processing : an applied subspace identification approach /
"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 algorith...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons, Inc.,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 037 | |a 9781119457787 |b Wiley | ||
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| 050 | 1 | 4 | |a TK5102.9 |b .C3195 2019 |
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| 100 | 1 | |a Candy, James V., |e author. | |
| 245 | 1 | 0 | |a Model-based processing : |b an applied subspace identification approach / |c James V. Candy, Lawrence Livermore National Laboratory, University of California Santa Barbara. |
| 264 | 1 | |a Hoboken, NJ : |b John Wiley & Sons, Inc., |c 2019. | |
| 264 | 4 | |c ©2019 | |
| 300 | |a 1 online resource (xxv, 511 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b n |2 rdamedia | ||
| 338 | |a online resource |b nc |2 rdacarrier | ||
| 504 | |a Includes 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"-- |c Provided by publisher. | ||
| 588 | 0 | |a Online resource; title from digital title page (viewed on April 01, 2019). | |
| 505 | 0 | |a Cover; 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 | |a 2.5.1 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 | |a Chapter 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 | |a 3.9 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 | |a 4.3.3 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 | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Signal processing |x Digital techniques |x Mathematics. | |
| 650 | 0 | |a Automatic control |x Mathematical models. | |
| 650 | 0 | |a Invariant subspaces. | |
| 650 | 6 | |a Traitement du signal |x Techniques numériques |x Mathématiques. | |
| 650 | 6 | |a Commande automatique |x Modèles mathématiques. | |
| 650 | 6 | |a Sous-espaces invariants. | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Mechanical. |2 bisacsh | |
| 650 | 7 | |a Automatic control |x Mathematical models |2 fast | |
| 650 | 7 | |a Invariant subspaces |2 fast | |
| 650 | 7 | |a Signal processing |x Digital techniques |x Mathematics |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Candy, James V. |t Model-based processing. |d Hoboken, NJ : John Wiley & Sons, Inc., [2018] |z 9781119457763 |w (DLC) 2018044855 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781119457763/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a IEEE |b IEEE |n 8788351 | ||
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| 938 | |a Recorded Books, LLC |b RECE |n rbeEB00756754 | ||
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