Nonlinear filters: theory and applications
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ, USA
Wiley
2022
|
| Schlagworte: | |
| Online-Zugang: | DE-Aug4 DE-573 DE-91 Volltext Volltext |
| Beschreibung: | 1 Online-Ressource (xxii, 273 Seiten) |
| ISBN: | 9781119078166 9781119078180 9781119078159 |
Internformat
MARC
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| 245 | 1 | 0 | |a Nonlinear filters |b theory and applications |c Peyman Setoodeh, Saeid Habibi, Simon Haykin |
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| 505 | 8 | |a Cover -- Title Page -- Copyright -- Contents -- List of Figures -- List of Table -- Preface -- Acknowledgments -- Acronyms -- Chapter 1 Introduction -- 1.1 State of a Dynamic System -- 1.2 State Estimation -- 1.3 Construals of Computing -- 1.4 Statistical Modeling -- 1.5 Vision for the Book -- Chapter 2 Observability -- 2.1 Introduction -- 2.2 State‐Space Model -- 2.3 The Concept of Observability -- 2.4 Observability of Linear Time‐Invariant Systems -- 2.4.1 Continuous‐Time LTI Systems -- 2.4.2 Discrete‐Time LTI Systems -- 2.4.3 Discretization of LTI Systems -- 2.5 Observability of Linear Time‐Varying Systems -- 2.5.1 Continuous‐Time LTV Systems -- 2.5.2 Discrete‐Time LTV Systems -- 2.5.3 Discretization of LTV Systems -- 2.6 Observability of Nonlinear Systems -- 2.6.1 Continuous‐Time Nonlinear Systems -- 2.6.2 Discrete‐Time Nonlinear Systems -- 2.6.3 Discretization of Nonlinear Systems -- 2.7 Observability of Stochastic Systems -- 2.8 Degree of Observability -- 2.9 Invertibility -- 2.10 Concluding Remarks -- Chapter 3 Observers -- 3.1 Introduction -- 3.2 Luenberger Observer -- 3.3 Extended Luenberger‐Type Observer -- 3.4 Sliding‐Mode Observer -- 3.5 Unknown‐Input Observer -- 3.6 Concluding Remarks -- Chapter 4 Bayesian Paradigm and Optimal Nonlinear Filtering -- 4.1 Introduction -- 4.2 Bayes' Rule -- 4.3 Optimal Nonlinear Filtering -- 4.4 Fisher Information -- 4.5 Posterior Cramér-Rao Lower Bound -- 4.6 Concluding Remarks -- Chapter 5 Kalman Filter -- 5.1 Introduction -- 5.2 Kalman Filter -- 5.3 Kalman Smoother -- 5.4 Information Filter -- 5.5 Extended Kalman Filter -- 5.6 Extended Information Filter -- 5.7 Divided‐Difference Filter -- 5.8 Unscented Kalman Filter -- 5.9 Cubature Kalman Filter -- 5.10 Generalized PID Filter -- 5.11 Gaussian‐Sum Filter -- 5.12 Applications -- 5.12.1 Information Fusion -- 5.12.2 Augmented Reality | |
| 505 | 8 | |a 5.12.3 Urban Traffic Network -- 5.12.4 Cybersecurity of Power Systems -- 5.12.5 Incidence of Influenza -- 5.12.6 COVID‐19 Pandemic -- 5.13 Concluding Remarks -- Chapter 6 Particle Filter -- 6.1 Introduction -- 6.2 Monte Carlo Method -- 6.3 Importance Sampling -- 6.4 Sequential Importance Sampling -- 6.5 Resampling -- 6.6 Sample Impoverishment -- 6.7 Choosing the Proposal Distribution -- 6.8 Generic Particle Filter -- 6.9 Applications -- 6.9.1 Simultaneous Localization and Mapping -- 6.10 Concluding Remarks -- Chapter 7 Smooth Variable‐Structure Filter -- 7.1 Introduction -- 7.2 The Switching Gain -- 7.3 Stability Analysis -- 7.4 Smoothing Subspace -- 7.5 Filter Corrective Term for Linear Systems -- 7.6 Filter Corrective Term for Nonlinear Systems -- 7.7 Bias Compensation -- 7.8 The Secondary Performance Indicator -- 7.9 Second‐Order Smooth Variable Structure Filter -- 7.10 Optimal Smoothing Boundary Design -- 7.11 Combination of SVSF with Other Filters -- 7.12 Applications -- 7.12.1 Multiple Target Tracking -- 7.12.2 Battery State‐of‐Charge Estimation -- 7.12.3 Robotics -- 7.13 Concluding Remarks -- Chapter 8 Deep Learning -- 8.1 Introduction -- 8.2 Gradient Descent -- 8.3 Stochastic Gradient Descent -- 8.4 Natural Gradient Descent -- 8.5 Neural Networks -- 8.6 Backpropagation -- 8.7 Backpropagation Through Time -- 8.8 Regularization -- 8.9 Initialization -- 8.10 Convolutional Neural Network -- 8.11 Long Short‐Term Memory -- 8.12 Hebbian Learning -- 8.13 Gibbs Sampling -- 8.14 Boltzmann Machine -- 8.15 Autoencoder -- 8.16 Generative Adversarial Network -- 8.17 Transformer -- 8.18 Concluding Remarks -- Chapter 9 Deep Learning‐Based Filters -- 9.1 Introduction -- 9.2 Variational Inference -- 9.3 Amortized Variational Inference -- 9.4 Deep Kalman Filter -- 9.5 Backpropagation Kalman Filter -- 9.6 Differentiable Particle Filter | |
| 505 | 8 | |a 9.7 Deep Rao-Blackwellized Particle Filter -- 9.8 Deep Variational Bayes Filter -- 9.9 Kalman Variational Autoencoder -- 9.10 Deep Variational Information Bottleneck -- 9.11 Wasserstein Distributionally Robust Kalman Filter -- 9.12 Hierarchical Invertible Neural Transport -- 9.13 Applications -- 9.13.1 Prediction of Drug Effect -- 9.13.2 Autonomous Driving -- 9.14 Concluding Remarks -- Chapter 10 Expectation Maximization -- 10.1 Introduction -- 10.2 Expectation Maximization Algorithm -- 10.3 Particle Expectation Maximization -- 10.4 Expectation Maximization for Gaussian Mixture Models -- 10.5 Neural Expectation Maximization -- 10.6 Relational Neural Expectation Maximization -- 10.7 Variational Filtering Expectation Maximization -- 10.8 Amortized Variational Filtering Expectation Maximization -- 10.9 Applications -- 10.9.1 Stochastic Volatility -- 10.9.2 Physical Reasoning -- 10.9.3 Speech, Music, and Video Modeling -- 10.10 Concluding Remarks -- Chapter 11 Reinforcement Learning‐Based Filter -- 11.1 Introduction -- 11.2 Reinforcement Learning -- 11.3 Variational Inference as Reinforcement Learning -- 11.4 Application -- 11.4.1 Battery State‐of‐Charge Estimation -- 11.5 Concluding Remarks -- Chapter 12 Nonparametric Bayesian Models -- 12.1 Introduction -- 12.2 Parametric vs Nonparametric Models -- 12.3 Measure‐Theoretic Probability -- 12.4 Exchangeability -- 12.5 Kolmogorov Extension Theorem -- 12.6 Extension of Bayesian Models -- 12.7 Conjugacy -- 12.8 Construction of Nonparametric Bayesian Models -- 12.9 Posterior Computability -- 12.10 Algorithmic Sufficiency -- 12.11 Applications -- 12.11.1 Multiple Object Tracking -- 12.11.2 Data‐Driven Probabilistic Optimal Power Flow -- 12.11.3 Analyzing Single‐Molecule Tracks -- 12.12 Concluding Remarks -- References -- Index -- EULA. | |
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Datensatz im Suchindex
| _version_ | 1805075414786244608 |
|---|---|
| adam_text | |
| adam_txt | |
| any_adam_object | |
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| author | Setoodeh, Peyman Habibi, Saeid Haykin, Simon S. 1931- |
| author_GND | (DE-588)128698497 |
| author_facet | Setoodeh, Peyman Habibi, Saeid Haykin, Simon S. 1931- |
| author_role | aut aut aut |
| author_sort | Setoodeh, Peyman |
| author_variant | p s ps s h sh s s h ss ssh |
| building | Verbundindex |
| bvnumber | BV048222001 |
| classification_rvk | ZN 5760 QH 237 SK 830 |
| collection | ZDB-35-WIC ZDB-30-PQE ZDB-35-IWT |
| contents | Cover -- Title Page -- Copyright -- Contents -- List of Figures -- List of Table -- Preface -- Acknowledgments -- Acronyms -- Chapter 1 Introduction -- 1.1 State of a Dynamic System -- 1.2 State Estimation -- 1.3 Construals of Computing -- 1.4 Statistical Modeling -- 1.5 Vision for the Book -- Chapter 2 Observability -- 2.1 Introduction -- 2.2 State‐Space Model -- 2.3 The Concept of Observability -- 2.4 Observability of Linear Time‐Invariant Systems -- 2.4.1 Continuous‐Time LTI Systems -- 2.4.2 Discrete‐Time LTI Systems -- 2.4.3 Discretization of LTI Systems -- 2.5 Observability of Linear Time‐Varying Systems -- 2.5.1 Continuous‐Time LTV Systems -- 2.5.2 Discrete‐Time LTV Systems -- 2.5.3 Discretization of LTV Systems -- 2.6 Observability of Nonlinear Systems -- 2.6.1 Continuous‐Time Nonlinear Systems -- 2.6.2 Discrete‐Time Nonlinear Systems -- 2.6.3 Discretization of Nonlinear Systems -- 2.7 Observability of Stochastic Systems -- 2.8 Degree of Observability -- 2.9 Invertibility -- 2.10 Concluding Remarks -- Chapter 3 Observers -- 3.1 Introduction -- 3.2 Luenberger Observer -- 3.3 Extended Luenberger‐Type Observer -- 3.4 Sliding‐Mode Observer -- 3.5 Unknown‐Input Observer -- 3.6 Concluding Remarks -- Chapter 4 Bayesian Paradigm and Optimal Nonlinear Filtering -- 4.1 Introduction -- 4.2 Bayes' Rule -- 4.3 Optimal Nonlinear Filtering -- 4.4 Fisher Information -- 4.5 Posterior Cramér-Rao Lower Bound -- 4.6 Concluding Remarks -- Chapter 5 Kalman Filter -- 5.1 Introduction -- 5.2 Kalman Filter -- 5.3 Kalman Smoother -- 5.4 Information Filter -- 5.5 Extended Kalman Filter -- 5.6 Extended Information Filter -- 5.7 Divided‐Difference Filter -- 5.8 Unscented Kalman Filter -- 5.9 Cubature Kalman Filter -- 5.10 Generalized PID Filter -- 5.11 Gaussian‐Sum Filter -- 5.12 Applications -- 5.12.1 Information Fusion -- 5.12.2 Augmented Reality 5.12.3 Urban Traffic Network -- 5.12.4 Cybersecurity of Power Systems -- 5.12.5 Incidence of Influenza -- 5.12.6 COVID‐19 Pandemic -- 5.13 Concluding Remarks -- Chapter 6 Particle Filter -- 6.1 Introduction -- 6.2 Monte Carlo Method -- 6.3 Importance Sampling -- 6.4 Sequential Importance Sampling -- 6.5 Resampling -- 6.6 Sample Impoverishment -- 6.7 Choosing the Proposal Distribution -- 6.8 Generic Particle Filter -- 6.9 Applications -- 6.9.1 Simultaneous Localization and Mapping -- 6.10 Concluding Remarks -- Chapter 7 Smooth Variable‐Structure Filter -- 7.1 Introduction -- 7.2 The Switching Gain -- 7.3 Stability Analysis -- 7.4 Smoothing Subspace -- 7.5 Filter Corrective Term for Linear Systems -- 7.6 Filter Corrective Term for Nonlinear Systems -- 7.7 Bias Compensation -- 7.8 The Secondary Performance Indicator -- 7.9 Second‐Order Smooth Variable Structure Filter -- 7.10 Optimal Smoothing Boundary Design -- 7.11 Combination of SVSF with Other Filters -- 7.12 Applications -- 7.12.1 Multiple Target Tracking -- 7.12.2 Battery State‐of‐Charge Estimation -- 7.12.3 Robotics -- 7.13 Concluding Remarks -- Chapter 8 Deep Learning -- 8.1 Introduction -- 8.2 Gradient Descent -- 8.3 Stochastic Gradient Descent -- 8.4 Natural Gradient Descent -- 8.5 Neural Networks -- 8.6 Backpropagation -- 8.7 Backpropagation Through Time -- 8.8 Regularization -- 8.9 Initialization -- 8.10 Convolutional Neural Network -- 8.11 Long Short‐Term Memory -- 8.12 Hebbian Learning -- 8.13 Gibbs Sampling -- 8.14 Boltzmann Machine -- 8.15 Autoencoder -- 8.16 Generative Adversarial Network -- 8.17 Transformer -- 8.18 Concluding Remarks -- Chapter 9 Deep Learning‐Based Filters -- 9.1 Introduction -- 9.2 Variational Inference -- 9.3 Amortized Variational Inference -- 9.4 Deep Kalman Filter -- 9.5 Backpropagation Kalman Filter -- 9.6 Differentiable Particle Filter 9.7 Deep Rao-Blackwellized Particle Filter -- 9.8 Deep Variational Bayes Filter -- 9.9 Kalman Variational Autoencoder -- 9.10 Deep Variational Information Bottleneck -- 9.11 Wasserstein Distributionally Robust Kalman Filter -- 9.12 Hierarchical Invertible Neural Transport -- 9.13 Applications -- 9.13.1 Prediction of Drug Effect -- 9.13.2 Autonomous Driving -- 9.14 Concluding Remarks -- Chapter 10 Expectation Maximization -- 10.1 Introduction -- 10.2 Expectation Maximization Algorithm -- 10.3 Particle Expectation Maximization -- 10.4 Expectation Maximization for Gaussian Mixture Models -- 10.5 Neural Expectation Maximization -- 10.6 Relational Neural Expectation Maximization -- 10.7 Variational Filtering Expectation Maximization -- 10.8 Amortized Variational Filtering Expectation Maximization -- 10.9 Applications -- 10.9.1 Stochastic Volatility -- 10.9.2 Physical Reasoning -- 10.9.3 Speech, Music, and Video Modeling -- 10.10 Concluding Remarks -- Chapter 11 Reinforcement Learning‐Based Filter -- 11.1 Introduction -- 11.2 Reinforcement Learning -- 11.3 Variational Inference as Reinforcement Learning -- 11.4 Application -- 11.4.1 Battery State‐of‐Charge Estimation -- 11.5 Concluding Remarks -- Chapter 12 Nonparametric Bayesian Models -- 12.1 Introduction -- 12.2 Parametric vs Nonparametric Models -- 12.3 Measure‐Theoretic Probability -- 12.4 Exchangeability -- 12.5 Kolmogorov Extension Theorem -- 12.6 Extension of Bayesian Models -- 12.7 Conjugacy -- 12.8 Construction of Nonparametric Bayesian Models -- 12.9 Posterior Computability -- 12.10 Algorithmic Sufficiency -- 12.11 Applications -- 12.11.1 Multiple Object Tracking -- 12.11.2 Data‐Driven Probabilistic Optimal Power Flow -- 12.11.3 Analyzing Single‐Molecule Tracks -- 12.12 Concluding Remarks -- References -- Index -- EULA. |
| ctrlnum | (ZDB-30-PQE)EBC6913237 (ZDB-30-PAD)EBC6913237 (ZDB-89-EBL)EBL6913237 (OCoLC)1303087403 (DE-599)BVBBV048222001 |
| dewey-full | 519.544 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.544 |
| dewey-search | 519.544 |
| dewey-sort | 3519.544 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Elektrotechnik / Elektronik / Nachrichtentechnik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Elektrotechnik / Elektronik / Nachrichtentechnik Wirtschaftswissenschaften |
| format | Electronic eBook |
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Vision for the Book -- Chapter 2 Observability -- 2.1 Introduction -- 2.2 State‐Space Model -- 2.3 The Concept of Observability -- 2.4 Observability of Linear Time‐Invariant Systems -- 2.4.1 Continuous‐Time LTI Systems -- 2.4.2 Discrete‐Time LTI Systems -- 2.4.3 Discretization of LTI Systems -- 2.5 Observability of Linear Time‐Varying Systems -- 2.5.1 Continuous‐Time LTV Systems -- 2.5.2 Discrete‐Time LTV Systems -- 2.5.3 Discretization of LTV Systems -- 2.6 Observability of Nonlinear Systems -- 2.6.1 Continuous‐Time Nonlinear Systems -- 2.6.2 Discrete‐Time Nonlinear Systems -- 2.6.3 Discretization of Nonlinear Systems -- 2.7 Observability of Stochastic Systems -- 2.8 Degree of Observability -- 2.9 Invertibility -- 2.10 Concluding Remarks -- Chapter 3 Observers -- 3.1 Introduction -- 3.2 Luenberger Observer -- 3.3 Extended Luenberger‐Type Observer -- 3.4 Sliding‐Mode Observer -- 3.5 Unknown‐Input Observer -- 3.6 Concluding Remarks -- Chapter 4 Bayesian Paradigm and Optimal Nonlinear Filtering -- 4.1 Introduction -- 4.2 Bayes' Rule -- 4.3 Optimal Nonlinear Filtering -- 4.4 Fisher Information -- 4.5 Posterior Cramér-Rao Lower Bound -- 4.6 Concluding Remarks -- Chapter 5 Kalman Filter -- 5.1 Introduction -- 5.2 Kalman Filter -- 5.3 Kalman Smoother -- 5.4 Information Filter -- 5.5 Extended Kalman Filter -- 5.6 Extended Information Filter -- 5.7 Divided‐Difference Filter -- 5.8 Unscented Kalman Filter -- 5.9 Cubature Kalman Filter -- 5.10 Generalized PID Filter -- 5.11 Gaussian‐Sum Filter -- 5.12 Applications -- 5.12.1 Information Fusion -- 5.12.2 Augmented Reality</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">5.12.3 Urban Traffic Network -- 5.12.4 Cybersecurity of Power Systems -- 5.12.5 Incidence of Influenza -- 5.12.6 COVID‐19 Pandemic -- 5.13 Concluding Remarks -- Chapter 6 Particle Filter -- 6.1 Introduction -- 6.2 Monte Carlo Method -- 6.3 Importance Sampling -- 6.4 Sequential Importance Sampling -- 6.5 Resampling -- 6.6 Sample Impoverishment -- 6.7 Choosing the Proposal Distribution -- 6.8 Generic Particle Filter -- 6.9 Applications -- 6.9.1 Simultaneous Localization and Mapping -- 6.10 Concluding Remarks -- Chapter 7 Smooth Variable‐Structure Filter -- 7.1 Introduction -- 7.2 The Switching Gain -- 7.3 Stability Analysis -- 7.4 Smoothing Subspace -- 7.5 Filter Corrective Term for Linear Systems -- 7.6 Filter Corrective Term for Nonlinear Systems -- 7.7 Bias Compensation -- 7.8 The Secondary Performance Indicator -- 7.9 Second‐Order Smooth Variable Structure Filter -- 7.10 Optimal Smoothing Boundary Design -- 7.11 Combination of SVSF with Other Filters -- 7.12 Applications -- 7.12.1 Multiple Target Tracking -- 7.12.2 Battery State‐of‐Charge Estimation -- 7.12.3 Robotics -- 7.13 Concluding Remarks -- Chapter 8 Deep Learning -- 8.1 Introduction -- 8.2 Gradient Descent -- 8.3 Stochastic Gradient Descent -- 8.4 Natural Gradient Descent -- 8.5 Neural Networks -- 8.6 Backpropagation -- 8.7 Backpropagation Through Time -- 8.8 Regularization -- 8.9 Initialization -- 8.10 Convolutional Neural Network -- 8.11 Long Short‐Term Memory -- 8.12 Hebbian Learning -- 8.13 Gibbs Sampling -- 8.14 Boltzmann Machine -- 8.15 Autoencoder -- 8.16 Generative Adversarial Network -- 8.17 Transformer -- 8.18 Concluding Remarks -- Chapter 9 Deep Learning‐Based Filters -- 9.1 Introduction -- 9.2 Variational Inference -- 9.3 Amortized Variational Inference -- 9.4 Deep Kalman Filter -- 9.5 Backpropagation Kalman Filter -- 9.6 Differentiable Particle Filter</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">9.7 Deep Rao-Blackwellized Particle Filter -- 9.8 Deep Variational Bayes Filter -- 9.9 Kalman Variational Autoencoder -- 9.10 Deep Variational Information Bottleneck -- 9.11 Wasserstein Distributionally Robust Kalman Filter -- 9.12 Hierarchical Invertible Neural Transport -- 9.13 Applications -- 9.13.1 Prediction of Drug Effect -- 9.13.2 Autonomous Driving -- 9.14 Concluding Remarks -- Chapter 10 Expectation Maximization -- 10.1 Introduction -- 10.2 Expectation Maximization Algorithm -- 10.3 Particle Expectation Maximization -- 10.4 Expectation Maximization for Gaussian Mixture Models -- 10.5 Neural Expectation Maximization -- 10.6 Relational Neural Expectation Maximization -- 10.7 Variational Filtering Expectation Maximization -- 10.8 Amortized Variational Filtering Expectation Maximization -- 10.9 Applications -- 10.9.1 Stochastic Volatility -- 10.9.2 Physical Reasoning -- 10.9.3 Speech, Music, and Video Modeling -- 10.10 Concluding Remarks -- Chapter 11 Reinforcement Learning‐Based Filter -- 11.1 Introduction -- 11.2 Reinforcement Learning -- 11.3 Variational Inference as Reinforcement Learning -- 11.4 Application -- 11.4.1 Battery State‐of‐Charge Estimation -- 11.5 Concluding Remarks -- Chapter 12 Nonparametric Bayesian Models -- 12.1 Introduction -- 12.2 Parametric vs Nonparametric Models -- 12.3 Measure‐Theoretic Probability -- 12.4 Exchangeability -- 12.5 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| id | DE-604.BV048222001 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:50:33Z |
| indexdate | 2024-07-20T05:41:01Z |
| institution | BVB |
| isbn | 9781119078166 9781119078180 9781119078159 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033602738 |
| oclc_num | 1303087403 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM DE-83 DE-573 DE-Aug4 |
| owner_facet | DE-91 DE-BY-TUM DE-83 DE-573 DE-Aug4 |
| physical | 1 Online-Ressource (xxii, 273 Seiten) |
| psigel | ZDB-35-WIC ZDB-30-PQE ZDB-35-IWT ZDB-35-WIC FHA_PDA_WIC_Kauf ZDB-30-PQE TUM_PDA_PQE_Kauf_2024 |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Wiley |
| record_format | marc |
| spelling | Setoodeh, Peyman Verfasser aut Nonlinear filters theory and applications Peyman Setoodeh, Saeid Habibi, Simon Haykin Hoboken, NJ, USA Wiley 2022 © 2022 1 Online-Ressource (xxii, 273 Seiten) txt rdacontent c rdamedia cr rdacarrier Cover -- Title Page -- Copyright -- Contents -- List of Figures -- List of Table -- Preface -- Acknowledgments -- Acronyms -- Chapter 1 Introduction -- 1.1 State of a Dynamic System -- 1.2 State Estimation -- 1.3 Construals of Computing -- 1.4 Statistical Modeling -- 1.5 Vision for the Book -- Chapter 2 Observability -- 2.1 Introduction -- 2.2 State‐Space Model -- 2.3 The Concept of Observability -- 2.4 Observability of Linear Time‐Invariant Systems -- 2.4.1 Continuous‐Time LTI Systems -- 2.4.2 Discrete‐Time LTI Systems -- 2.4.3 Discretization of LTI Systems -- 2.5 Observability of Linear Time‐Varying Systems -- 2.5.1 Continuous‐Time LTV Systems -- 2.5.2 Discrete‐Time LTV Systems -- 2.5.3 Discretization of LTV Systems -- 2.6 Observability of Nonlinear Systems -- 2.6.1 Continuous‐Time Nonlinear Systems -- 2.6.2 Discrete‐Time Nonlinear Systems -- 2.6.3 Discretization of Nonlinear Systems -- 2.7 Observability of Stochastic Systems -- 2.8 Degree of Observability -- 2.9 Invertibility -- 2.10 Concluding Remarks -- Chapter 3 Observers -- 3.1 Introduction -- 3.2 Luenberger Observer -- 3.3 Extended Luenberger‐Type Observer -- 3.4 Sliding‐Mode Observer -- 3.5 Unknown‐Input Observer -- 3.6 Concluding Remarks -- Chapter 4 Bayesian Paradigm and Optimal Nonlinear Filtering -- 4.1 Introduction -- 4.2 Bayes' Rule -- 4.3 Optimal Nonlinear Filtering -- 4.4 Fisher Information -- 4.5 Posterior Cramér-Rao Lower Bound -- 4.6 Concluding Remarks -- Chapter 5 Kalman Filter -- 5.1 Introduction -- 5.2 Kalman Filter -- 5.3 Kalman Smoother -- 5.4 Information Filter -- 5.5 Extended Kalman Filter -- 5.6 Extended Information Filter -- 5.7 Divided‐Difference Filter -- 5.8 Unscented Kalman Filter -- 5.9 Cubature Kalman Filter -- 5.10 Generalized PID Filter -- 5.11 Gaussian‐Sum Filter -- 5.12 Applications -- 5.12.1 Information Fusion -- 5.12.2 Augmented Reality 5.12.3 Urban Traffic Network -- 5.12.4 Cybersecurity of Power Systems -- 5.12.5 Incidence of Influenza -- 5.12.6 COVID‐19 Pandemic -- 5.13 Concluding Remarks -- Chapter 6 Particle Filter -- 6.1 Introduction -- 6.2 Monte Carlo Method -- 6.3 Importance Sampling -- 6.4 Sequential Importance Sampling -- 6.5 Resampling -- 6.6 Sample Impoverishment -- 6.7 Choosing the Proposal Distribution -- 6.8 Generic Particle Filter -- 6.9 Applications -- 6.9.1 Simultaneous Localization and Mapping -- 6.10 Concluding Remarks -- Chapter 7 Smooth Variable‐Structure Filter -- 7.1 Introduction -- 7.2 The Switching Gain -- 7.3 Stability Analysis -- 7.4 Smoothing Subspace -- 7.5 Filter Corrective Term for Linear Systems -- 7.6 Filter Corrective Term for Nonlinear Systems -- 7.7 Bias Compensation -- 7.8 The Secondary Performance Indicator -- 7.9 Second‐Order Smooth Variable Structure Filter -- 7.10 Optimal Smoothing Boundary Design -- 7.11 Combination of SVSF with Other Filters -- 7.12 Applications -- 7.12.1 Multiple Target Tracking -- 7.12.2 Battery State‐of‐Charge Estimation -- 7.12.3 Robotics -- 7.13 Concluding Remarks -- Chapter 8 Deep Learning -- 8.1 Introduction -- 8.2 Gradient Descent -- 8.3 Stochastic Gradient Descent -- 8.4 Natural Gradient Descent -- 8.5 Neural Networks -- 8.6 Backpropagation -- 8.7 Backpropagation Through Time -- 8.8 Regularization -- 8.9 Initialization -- 8.10 Convolutional Neural Network -- 8.11 Long Short‐Term Memory -- 8.12 Hebbian Learning -- 8.13 Gibbs Sampling -- 8.14 Boltzmann Machine -- 8.15 Autoencoder -- 8.16 Generative Adversarial Network -- 8.17 Transformer -- 8.18 Concluding Remarks -- Chapter 9 Deep Learning‐Based Filters -- 9.1 Introduction -- 9.2 Variational Inference -- 9.3 Amortized Variational Inference -- 9.4 Deep Kalman Filter -- 9.5 Backpropagation Kalman Filter -- 9.6 Differentiable Particle Filter 9.7 Deep Rao-Blackwellized Particle Filter -- 9.8 Deep Variational Bayes Filter -- 9.9 Kalman Variational Autoencoder -- 9.10 Deep Variational Information Bottleneck -- 9.11 Wasserstein Distributionally Robust Kalman Filter -- 9.12 Hierarchical Invertible Neural Transport -- 9.13 Applications -- 9.13.1 Prediction of Drug Effect -- 9.13.2 Autonomous Driving -- 9.14 Concluding Remarks -- Chapter 10 Expectation Maximization -- 10.1 Introduction -- 10.2 Expectation Maximization Algorithm -- 10.3 Particle Expectation Maximization -- 10.4 Expectation Maximization for Gaussian Mixture Models -- 10.5 Neural Expectation Maximization -- 10.6 Relational Neural Expectation Maximization -- 10.7 Variational Filtering Expectation Maximization -- 10.8 Amortized Variational Filtering Expectation Maximization -- 10.9 Applications -- 10.9.1 Stochastic Volatility -- 10.9.2 Physical Reasoning -- 10.9.3 Speech, Music, and Video Modeling -- 10.10 Concluding Remarks -- Chapter 11 Reinforcement Learning‐Based Filter -- 11.1 Introduction -- 11.2 Reinforcement Learning -- 11.3 Variational Inference as Reinforcement Learning -- 11.4 Application -- 11.4.1 Battery State‐of‐Charge Estimation -- 11.5 Concluding Remarks -- Chapter 12 Nonparametric Bayesian Models -- 12.1 Introduction -- 12.2 Parametric vs Nonparametric Models -- 12.3 Measure‐Theoretic Probability -- 12.4 Exchangeability -- 12.5 Kolmogorov Extension Theorem -- 12.6 Extension of Bayesian Models -- 12.7 Conjugacy -- 12.8 Construction of Nonparametric Bayesian Models -- 12.9 Posterior Computability -- 12.10 Algorithmic Sufficiency -- 12.11 Applications -- 12.11.1 Multiple Object Tracking -- 12.11.2 Data‐Driven Probabilistic Optimal Power Flow -- 12.11.3 Analyzing Single‐Molecule Tracks -- 12.12 Concluding Remarks -- References -- Index -- EULA. Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Signalverarbeitung (DE-588)4054947-1 gnd rswk-swf Nichtlineares Filter (DE-588)4451051-2 gnd rswk-swf Regelungstheorie (DE-588)4122327-5 gnd rswk-swf Algorithmus (DE-588)4001183-5 gnd rswk-swf Nichtlineares Filter (DE-588)4451051-2 s Algorithmus (DE-588)4001183-5 s Signalverarbeitung (DE-588)4054947-1 s Künstliche Intelligenz (DE-588)4033447-8 s Regelungstheorie (DE-588)4122327-5 s DE-604 Habibi, Saeid Verfasser aut Haykin, Simon S. 1931- Verfasser (DE-588)128698497 aut Erscheint auch als Druck-Ausgabe 978-1-118-83581-4 https://onlinelibrary.wiley.com/doi/book/10.1002/9781119078166 Verlag URL des Erstveröffentlichers Volltext https://ieeexplore.ieee.org/book/9740330 Aggregator URL des Erstveröffentlichers Volltext |
| spellingShingle | Setoodeh, Peyman Habibi, Saeid Haykin, Simon S. 1931- Nonlinear filters theory and applications Cover -- Title Page -- Copyright -- Contents -- List of Figures -- List of Table -- Preface -- Acknowledgments -- Acronyms -- Chapter 1 Introduction -- 1.1 State of a Dynamic System -- 1.2 State Estimation -- 1.3 Construals of Computing -- 1.4 Statistical Modeling -- 1.5 Vision for the Book -- Chapter 2 Observability -- 2.1 Introduction -- 2.2 State‐Space Model -- 2.3 The Concept of Observability -- 2.4 Observability of Linear Time‐Invariant Systems -- 2.4.1 Continuous‐Time LTI Systems -- 2.4.2 Discrete‐Time LTI Systems -- 2.4.3 Discretization of LTI Systems -- 2.5 Observability of Linear Time‐Varying Systems -- 2.5.1 Continuous‐Time LTV Systems -- 2.5.2 Discrete‐Time LTV Systems -- 2.5.3 Discretization of LTV Systems -- 2.6 Observability of Nonlinear Systems -- 2.6.1 Continuous‐Time Nonlinear Systems -- 2.6.2 Discrete‐Time Nonlinear Systems -- 2.6.3 Discretization of Nonlinear Systems -- 2.7 Observability of Stochastic Systems -- 2.8 Degree of Observability -- 2.9 Invertibility -- 2.10 Concluding Remarks -- Chapter 3 Observers -- 3.1 Introduction -- 3.2 Luenberger Observer -- 3.3 Extended Luenberger‐Type Observer -- 3.4 Sliding‐Mode Observer -- 3.5 Unknown‐Input Observer -- 3.6 Concluding Remarks -- Chapter 4 Bayesian Paradigm and Optimal Nonlinear Filtering -- 4.1 Introduction -- 4.2 Bayes' Rule -- 4.3 Optimal Nonlinear Filtering -- 4.4 Fisher Information -- 4.5 Posterior Cramér-Rao Lower Bound -- 4.6 Concluding Remarks -- Chapter 5 Kalman Filter -- 5.1 Introduction -- 5.2 Kalman Filter -- 5.3 Kalman Smoother -- 5.4 Information Filter -- 5.5 Extended Kalman Filter -- 5.6 Extended Information Filter -- 5.7 Divided‐Difference Filter -- 5.8 Unscented Kalman Filter -- 5.9 Cubature Kalman Filter -- 5.10 Generalized PID Filter -- 5.11 Gaussian‐Sum Filter -- 5.12 Applications -- 5.12.1 Information Fusion -- 5.12.2 Augmented Reality 5.12.3 Urban Traffic Network -- 5.12.4 Cybersecurity of Power Systems -- 5.12.5 Incidence of Influenza -- 5.12.6 COVID‐19 Pandemic -- 5.13 Concluding Remarks -- Chapter 6 Particle Filter -- 6.1 Introduction -- 6.2 Monte Carlo Method -- 6.3 Importance Sampling -- 6.4 Sequential Importance Sampling -- 6.5 Resampling -- 6.6 Sample Impoverishment -- 6.7 Choosing the Proposal Distribution -- 6.8 Generic Particle Filter -- 6.9 Applications -- 6.9.1 Simultaneous Localization and Mapping -- 6.10 Concluding Remarks -- Chapter 7 Smooth Variable‐Structure Filter -- 7.1 Introduction -- 7.2 The Switching Gain -- 7.3 Stability Analysis -- 7.4 Smoothing Subspace -- 7.5 Filter Corrective Term for Linear Systems -- 7.6 Filter Corrective Term for Nonlinear Systems -- 7.7 Bias Compensation -- 7.8 The Secondary Performance Indicator -- 7.9 Second‐Order Smooth Variable Structure Filter -- 7.10 Optimal Smoothing Boundary Design -- 7.11 Combination of SVSF with Other Filters -- 7.12 Applications -- 7.12.1 Multiple Target Tracking -- 7.12.2 Battery State‐of‐Charge Estimation -- 7.12.3 Robotics -- 7.13 Concluding Remarks -- Chapter 8 Deep Learning -- 8.1 Introduction -- 8.2 Gradient Descent -- 8.3 Stochastic Gradient Descent -- 8.4 Natural Gradient Descent -- 8.5 Neural Networks -- 8.6 Backpropagation -- 8.7 Backpropagation Through Time -- 8.8 Regularization -- 8.9 Initialization -- 8.10 Convolutional Neural Network -- 8.11 Long Short‐Term Memory -- 8.12 Hebbian Learning -- 8.13 Gibbs Sampling -- 8.14 Boltzmann Machine -- 8.15 Autoencoder -- 8.16 Generative Adversarial Network -- 8.17 Transformer -- 8.18 Concluding Remarks -- Chapter 9 Deep Learning‐Based Filters -- 9.1 Introduction -- 9.2 Variational Inference -- 9.3 Amortized Variational Inference -- 9.4 Deep Kalman Filter -- 9.5 Backpropagation Kalman Filter -- 9.6 Differentiable Particle Filter 9.7 Deep Rao-Blackwellized Particle Filter -- 9.8 Deep Variational Bayes Filter -- 9.9 Kalman Variational Autoencoder -- 9.10 Deep Variational Information Bottleneck -- 9.11 Wasserstein Distributionally Robust Kalman Filter -- 9.12 Hierarchical Invertible Neural Transport -- 9.13 Applications -- 9.13.1 Prediction of Drug Effect -- 9.13.2 Autonomous Driving -- 9.14 Concluding Remarks -- Chapter 10 Expectation Maximization -- 10.1 Introduction -- 10.2 Expectation Maximization Algorithm -- 10.3 Particle Expectation Maximization -- 10.4 Expectation Maximization for Gaussian Mixture Models -- 10.5 Neural Expectation Maximization -- 10.6 Relational Neural Expectation Maximization -- 10.7 Variational Filtering Expectation Maximization -- 10.8 Amortized Variational Filtering Expectation Maximization -- 10.9 Applications -- 10.9.1 Stochastic Volatility -- 10.9.2 Physical Reasoning -- 10.9.3 Speech, Music, and Video Modeling -- 10.10 Concluding Remarks -- Chapter 11 Reinforcement Learning‐Based Filter -- 11.1 Introduction -- 11.2 Reinforcement Learning -- 11.3 Variational Inference as Reinforcement Learning -- 11.4 Application -- 11.4.1 Battery State‐of‐Charge Estimation -- 11.5 Concluding Remarks -- Chapter 12 Nonparametric Bayesian Models -- 12.1 Introduction -- 12.2 Parametric vs Nonparametric Models -- 12.3 Measure‐Theoretic Probability -- 12.4 Exchangeability -- 12.5 Kolmogorov Extension Theorem -- 12.6 Extension of Bayesian Models -- 12.7 Conjugacy -- 12.8 Construction of Nonparametric Bayesian Models -- 12.9 Posterior Computability -- 12.10 Algorithmic Sufficiency -- 12.11 Applications -- 12.11.1 Multiple Object Tracking -- 12.11.2 Data‐Driven Probabilistic Optimal Power Flow -- 12.11.3 Analyzing Single‐Molecule Tracks -- 12.12 Concluding Remarks -- References -- Index -- EULA. Künstliche Intelligenz (DE-588)4033447-8 gnd Signalverarbeitung (DE-588)4054947-1 gnd Nichtlineares Filter (DE-588)4451051-2 gnd Regelungstheorie (DE-588)4122327-5 gnd Algorithmus (DE-588)4001183-5 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4054947-1 (DE-588)4451051-2 (DE-588)4122327-5 (DE-588)4001183-5 |
| title | Nonlinear filters theory and applications |
| title_auth | Nonlinear filters theory and applications |
| title_exact_search | Nonlinear filters theory and applications |
| title_exact_search_txtP | Nonlinear filters theory and applications |
| title_full | Nonlinear filters theory and applications Peyman Setoodeh, Saeid Habibi, Simon Haykin |
| title_fullStr | Nonlinear filters theory and applications Peyman Setoodeh, Saeid Habibi, Simon Haykin |
| title_full_unstemmed | Nonlinear filters theory and applications Peyman Setoodeh, Saeid Habibi, Simon Haykin |
| title_short | Nonlinear filters |
| title_sort | nonlinear filters theory and applications |
| title_sub | theory and applications |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Signalverarbeitung (DE-588)4054947-1 gnd Nichtlineares Filter (DE-588)4451051-2 gnd Regelungstheorie (DE-588)4122327-5 gnd Algorithmus (DE-588)4001183-5 gnd |
| topic_facet | Künstliche Intelligenz Signalverarbeitung Nichtlineares Filter Regelungstheorie Algorithmus |
| url | https://onlinelibrary.wiley.com/doi/book/10.1002/9781119078166 https://ieeexplore.ieee.org/book/9740330 |
| work_keys_str_mv | AT setoodehpeyman nonlinearfilterstheoryandapplications AT habibisaeid nonlinearfilterstheoryandapplications AT haykinsimons nonlinearfilterstheoryandapplications |