{"product_id":"nonlinear-filters-isbn-9781118835814","title":"Nonlinear Filters","description":"\u003cb\u003eNONLINEAR FILTERS\u003c\/b\u003e \u003cp\u003e\u003cb\u003eDiscover the utility of using deep learning and (deep) reinforcement learning in deriving filtering algorithms with this insightful and powerful new resource\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eNonlinear Filters: Theory and Applications \u003c\/i\u003edelivers an insightful view on state and parameter estimation by merging ideas from control theory, statistical signal processing, and machine learning. Taking an algorithmic approach, the book covers both classic and machine learning-based filtering algorithms.\u003c\/p\u003e \u003cp\u003eReaders of \u003ci\u003eNonlinear Filters\u003c\/i\u003e will greatly benefit from the wide spectrum of presented topics including stability, robustness, computability, and algorithmic sufficiency. Readers will also enjoy:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eOrganization that allows the book to act as a stand-alone, self-contained reference\u003c\/li\u003e \u003cli\u003eA thorough exploration of the notion of observability, nonlinear observers, and the theory of optimal nonlinear filtering that bridges the gap between different science and engineering disciplines\u003c\/li\u003e \u003cli\u003eA profound account of Bayesian filters including Kalman filter and its variants as well as particle filter\u003c\/li\u003e \u003cli\u003eA rigorous derivation of the smooth variable structure filter as a predictor-corrector estimator formulated based on a stability theorem, used to confine the estimated states within a neighborhood of their true values\u003c\/li\u003e \u003cli\u003eA concise tutorial on deep learning and reinforcement learning\u003c\/li\u003e \u003cli\u003eA detailed presentation of the expectation maximization algorithm and its machine learning-based variants, used for joint state and parameter estimation\u003c\/li\u003e \u003cli\u003eGuidelines for constructing nonparametric Bayesian models from parametric ones\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for researchers, professors, and graduate students in engineering, computer science, applied mathematics, and artificial intelligence, \u003ci\u003eNonlinear Filters: Theory and Applications\u003c\/i\u003e will also earn a place in the libraries of those studying or practicing in fields involving pandemic diseases, cybersecurity, information fusion, augmented reality, autonomous driving, urban traffic network, navigation and tracking, robotics, power systems, hybrid technologies, and finance.\u003c\/p\u003e \u003cp\u003eList of Figures xiii\u003c\/p\u003e \u003cp\u003eList of Table xv\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003eAcknowledgments xix\u003c\/p\u003e \u003cp\u003eAcronyms xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 State of a Dynamic System 1\u003c\/p\u003e \u003cp\u003e1.2 State Estimation 1\u003c\/p\u003e \u003cp\u003e1.3 Construals of Computing 2\u003c\/p\u003e \u003cp\u003e1.4 Statistical Modeling 3\u003c\/p\u003e \u003cp\u003e1.5 Vision for the Book 4\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Observability \u003c\/b\u003e\u003cb\u003e7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 7\u003c\/p\u003e \u003cp\u003e2.2 State-Space Model 7\u003c\/p\u003e \u003cp\u003e2.3 The Concept of Observability 9\u003c\/p\u003e \u003cp\u003e2.4 Observability of Linear Time-Invariant Systems 10\u003c\/p\u003e \u003cp\u003e2.4.1 Continuous-Time LTI Systems 10\u003c\/p\u003e \u003cp\u003e2.4.2 Discrete-Time LTI Systems 12\u003c\/p\u003e \u003cp\u003e2.4.3 Discretization of LTI Systems 14\u003c\/p\u003e \u003cp\u003e2.5 Observability of Linear Time-Varying Systems 14\u003c\/p\u003e \u003cp\u003e2.5.1 Continuous-Time LTV Systems 14\u003c\/p\u003e \u003cp\u003e2.5.2 Discrete-Time LTV Systems 16\u003c\/p\u003e \u003cp\u003e2.5.3 Discretization of LTV Systems 17\u003c\/p\u003e \u003cp\u003e2.6 Observability of Nonlinear Systems 17\u003c\/p\u003e \u003cp\u003e2.6.1 Continuous-Time Nonlinear Systems 18\u003c\/p\u003e \u003cp\u003e2.6.2 Discrete-Time Nonlinear Systems 21\u003c\/p\u003e \u003cp\u003e2.6.3 Discretization of Nonlinear Systems 22\u003c\/p\u003e \u003cp\u003e2.7 Observability of Stochastic Systems 23\u003c\/p\u003e \u003cp\u003e2.8 Degree of Observability 25\u003c\/p\u003e \u003cp\u003e2.9 Invertibility 26\u003c\/p\u003e \u003cp\u003e2.10 Concluding Remarks 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Observers \u003c\/b\u003e\u003cb\u003e29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 29\u003c\/p\u003e \u003cp\u003e3.2 Luenberger Observer 30\u003c\/p\u003e \u003cp\u003e3.3 Extended Luenberger-Type Observer 31\u003c\/p\u003e \u003cp\u003e3.4 Sliding-Mode Observer 33\u003c\/p\u003e \u003cp\u003e3.5 Unknown-Input Observer 35\u003c\/p\u003e \u003cp\u003e3.6 Concluding Remarks 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Bayesian Paradigm and Optimal Nonlinear Filtering \u003c\/b\u003e\u003cb\u003e41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 41\u003c\/p\u003e \u003cp\u003e4.2 Bayes’ Rule 42\u003c\/p\u003e \u003cp\u003e4.3 Optimal Nonlinear Filtering 42\u003c\/p\u003e \u003cp\u003e4.4 Fisher Information 45\u003c\/p\u003e \u003cp\u003e4.5 Posterior Cramér–Rao Lower Bound 46\u003c\/p\u003e \u003cp\u003e4.6 Concluding Remarks 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Kalman Filter \u003c\/b\u003e\u003cb\u003e49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 49\u003c\/p\u003e \u003cp\u003e5.2 Kalman Filter 50\u003c\/p\u003e \u003cp\u003e5.3 Kalman Smoother 53\u003c\/p\u003e \u003cp\u003e5.4 Information Filter 54\u003c\/p\u003e \u003cp\u003e5.5 Extended Kalman Filter 54\u003c\/p\u003e \u003cp\u003e5.6 Extended Information Filter 54\u003c\/p\u003e \u003cp\u003e5.7 Divided-Difference Filter 54\u003c\/p\u003e \u003cp\u003e5.8 Unscented Kalman Filter 60\u003c\/p\u003e \u003cp\u003e5.9 Cubature Kalman Filter 60\u003c\/p\u003e \u003cp\u003e5.10 Generalized PID Filter 64\u003c\/p\u003e \u003cp\u003e5.11 Gaussian-Sum Filter 65\u003c\/p\u003e \u003cp\u003e5.12 Applications 67\u003c\/p\u003e \u003cp\u003e5.12.1 Information Fusion 67\u003c\/p\u003e \u003cp\u003e5.12.2 Augmented Reality 67\u003c\/p\u003e \u003cp\u003e5.12.3 Urban Traffic Network 67\u003c\/p\u003e \u003cp\u003e5.12.4 Cybersecurity of Power Systems 67\u003c\/p\u003e \u003cp\u003e5.12.5 Incidence of Influenza 68\u003c\/p\u003e \u003cp\u003e5.12.6 COVID-19 Pandemic 68\u003c\/p\u003e \u003cp\u003e5.13 Concluding Remarks 70\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Particle Filter \u003c\/b\u003e\u003cb\u003e71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 71\u003c\/p\u003e \u003cp\u003e6.2 Monte Carlo Method 72\u003c\/p\u003e \u003cp\u003e6.3 Importance Sampling 72\u003c\/p\u003e \u003cp\u003e6.4 Sequential Importance Sampling 73\u003c\/p\u003e \u003cp\u003e6.5 Resampling 75\u003c\/p\u003e \u003cp\u003e6.6 Sample Impoverishment 76\u003c\/p\u003e \u003cp\u003e6.7 Choosing the Proposal Distribution 77\u003c\/p\u003e \u003cp\u003e6.8 Generic Particle Filter 78\u003c\/p\u003e \u003cp\u003e6.9 Applications 81\u003c\/p\u003e \u003cp\u003e6.9.1 Simultaneous Localization and Mapping 81\u003c\/p\u003e \u003cp\u003e6.10 Concluding Remarks 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Smooth Variable-Structure Filter \u003c\/b\u003e\u003cb\u003e85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 85\u003c\/p\u003e \u003cp\u003e7.2 The Switching Gain 86\u003c\/p\u003e \u003cp\u003e7.3 Stability Analysis 90\u003c\/p\u003e \u003cp\u003e7.4 Smoothing Subspace 93\u003c\/p\u003e \u003cp\u003e7.5 Filter Corrective Term for Linear Systems 96\u003c\/p\u003e \u003cp\u003e7.6 Filter Corrective Term for Nonlinear Systems 102\u003c\/p\u003e \u003cp\u003e7.7 Bias Compensation 105\u003c\/p\u003e \u003cp\u003e7.8 The Secondary Performance Indicator 107\u003c\/p\u003e \u003cp\u003e7.9 Second-Order Smooth Variable Structure Filter 108\u003c\/p\u003e \u003cp\u003e7.10 Optimal Smoothing Boundary Design 108\u003c\/p\u003e \u003cp\u003e7.11 Combination of SVSF with Other Filters 110\u003c\/p\u003e \u003cp\u003e7.12 Applications 110\u003c\/p\u003e \u003cp\u003e7.12.1 Multiple Target Tracking 111\u003c\/p\u003e \u003cp\u003e7.12.2 Battery State-of-Charge Estimation 111\u003c\/p\u003e \u003cp\u003e7.12.3 Robotics 111\u003c\/p\u003e \u003cp\u003e7.13 Concluding Remarks 111\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Deep Learning \u003c\/b\u003e\u003cb\u003e113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 113\u003c\/p\u003e \u003cp\u003e8.2 Gradient Descent 114\u003c\/p\u003e \u003cp\u003e8.3 Stochastic Gradient Descent 115\u003c\/p\u003e \u003cp\u003e8.4 Natural Gradient Descent 119\u003c\/p\u003e \u003cp\u003e8.5 Neural Networks 120\u003c\/p\u003e \u003cp\u003e8.6 Backpropagation 122\u003c\/p\u003e \u003cp\u003e8.7 Backpropagation Through Time 122\u003c\/p\u003e \u003cp\u003e8.8 Regularization 122\u003c\/p\u003e \u003cp\u003e8.9 Initialization 125\u003c\/p\u003e \u003cp\u003e8.10 Convolutional Neural Network 125\u003c\/p\u003e \u003cp\u003e8.11 Long Short-Term Memory 127\u003c\/p\u003e \u003cp\u003e8.12 Hebbian Learning 129\u003c\/p\u003e \u003cp\u003e8.13 Gibbs Sampling 131\u003c\/p\u003e \u003cp\u003e8.14 Boltzmann Machine 131\u003c\/p\u003e \u003cp\u003e8.15 Autoencoder 135\u003c\/p\u003e \u003cp\u003e8.16 Generative Adversarial Network 136\u003c\/p\u003e \u003cp\u003e8.17 Transformer 137\u003c\/p\u003e \u003cp\u003e8.18 Concluding Remarks 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Deep Learning-Based Filters \u003c\/b\u003e\u003cb\u003e141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 141\u003c\/p\u003e \u003cp\u003e9.2 Variational Inference 142\u003c\/p\u003e \u003cp\u003e9.3 Amortized Variational Inference 144\u003c\/p\u003e \u003cp\u003e9.4 Deep Kalman Filter 144\u003c\/p\u003e \u003cp\u003e9.5 Backpropagation Kalman Filter 146\u003c\/p\u003e \u003cp\u003e9.6 Differentiable Particle Filter 148\u003c\/p\u003e \u003cp\u003e9.7 Deep Rao–Blackwellized Particle Filter 152\u003c\/p\u003e \u003cp\u003e9.8 Deep Variational Bayes Filter 158\u003c\/p\u003e \u003cp\u003e9.9 Kalman Variational Autoencoder 167\u003c\/p\u003e \u003cp\u003e9.10 Deep Variational Information Bottleneck 172\u003c\/p\u003e \u003cp\u003e9.11 Wasserstein Distributionally Robust Kalman Filter 176\u003c\/p\u003e \u003cp\u003e9.12 Hierarchical Invertible Neural Transport 178\u003c\/p\u003e \u003cp\u003e9.13 Applications 182\u003c\/p\u003e \u003cp\u003e9.13.1 Prediction of Drug Effect 182\u003c\/p\u003e \u003cp\u003e9.13.2 Autonomous Driving 183\u003c\/p\u003e \u003cp\u003e9.14 Concluding Remarks 183\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Expectation Maximization \u003c\/b\u003e\u003cb\u003e185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 185\u003c\/p\u003e \u003cp\u003e10.2 Expectation Maximization Algorithm 185\u003c\/p\u003e \u003cp\u003e10.3 Particle Expectation Maximization 188\u003c\/p\u003e \u003cp\u003e10.4 Expectation Maximization for Gaussian Mixture Models 190\u003c\/p\u003e \u003cp\u003e10.5 Neural Expectation Maximization 191\u003c\/p\u003e \u003cp\u003e10.6 Relational Neural Expectation Maximization 194\u003c\/p\u003e \u003cp\u003e10.7 Variational Filtering Expectation Maximization 196\u003c\/p\u003e \u003cp\u003e10.8 Amortized Variational Filtering Expectation Maximization 198\u003c\/p\u003e \u003cp\u003e10.9 Applications 199\u003c\/p\u003e \u003cp\u003e10.9.1 Stochastic Volatility 199\u003c\/p\u003e \u003cp\u003e10.9.2 Physical Reasoning 200\u003c\/p\u003e \u003cp\u003e10.9.3 Speech, Music, and Video Modeling 200\u003c\/p\u003e \u003cp\u003e10.10 Concluding Remarks 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Reinforcement Learning-Based Filter \u003c\/b\u003e\u003cb\u003e203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 203\u003c\/p\u003e \u003cp\u003e11.2 Reinforcement Learning 204\u003c\/p\u003e \u003cp\u003e11.3 Variational Inference as Reinforcement Learning 207\u003c\/p\u003e \u003cp\u003e11.4 Application 210\u003c\/p\u003e \u003cp\u003e11.4.1 Battery State-of-Charge Estimation 210\u003c\/p\u003e \u003cp\u003e11.5 Concluding Remarks 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Nonparametric Bayesian Models \u003c\/b\u003e\u003cb\u003e213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 213\u003c\/p\u003e \u003cp\u003e12.2 Parametric vs Nonparametric Models 213\u003c\/p\u003e \u003cp\u003e12.3 Measure-Theoretic Probability 214\u003c\/p\u003e \u003cp\u003e12.4 Exchangeability 219\u003c\/p\u003e \u003cp\u003e12.5 Kolmogorov Extension Theorem 221\u003c\/p\u003e \u003cp\u003e12.6 Extension of Bayesian Models 223\u003c\/p\u003e \u003cp\u003e12.7 Conjugacy 224\u003c\/p\u003e \u003cp\u003e12.8 Construction of Nonparametric Bayesian Models 226\u003c\/p\u003e \u003cp\u003e12.9 Posterior Computability 227\u003c\/p\u003e \u003cp\u003e12.10 Algorithmic Sufficiency 228\u003c\/p\u003e \u003cp\u003e12.11 Applications 232\u003c\/p\u003e \u003cp\u003e12.11.1 Multiple Object Tracking 233\u003c\/p\u003e \u003cp\u003e12.11.2 Data-Driven Probabilistic Optimal Power Flow 233\u003c\/p\u003e \u003cp\u003e12.11.3 Analyzing Single-Molecule Tracks 233\u003c\/p\u003e \u003cp\u003e12.12 Concluding Remarks 233\u003c\/p\u003e \u003cp\u003eReferences 235\u003c\/p\u003e \u003cp\u003eIndex 253\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePeyman Setoodeh, PhD,\u003c\/b\u003e is Visiting Professor with the Centre for Mechatronics and Hybrid Technologies (CMHT) at McMaster University. He is a Senior Member of the IEEE.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSaeid Habibi, PhD,\u003c\/b\u003e is Professor and former Chair of the Department of Mechanical Engineering and the Director of the Centre for Mechatronics and Hybrid Technologies (CMHT) at McMaster University. He is a Fellow of the ASME and the CSME as well as a Canada Research Chair and a Senior NSERC Industrial Research Chair. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eSimon Haykin, PhD,\u003c\/b\u003e is Distinguished University Professor with the Department of Electrical and Computer Engineering and the Director of the Cognitive Systems Laboratory (CSL) at McMaster University. He is a Fellow of the IEEE and the Royal Society of Canada. He is a recipient of the Henry Booker Gold Medal from the International Union of Radio Science, the IEEE James H. Mulligan Jr. Education Medal, and the IEEE Denis J. Picard Medal for Radar Technologies and Applications.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDiscover the utility of using deep learning and (deep) reinforcement learning in deriving filtering algorithms with this insightful and powerful new resource\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eNonlinear Filters: Theory and Applications \u003c\/i\u003edelivers an insightful view on state and parameter estimation by merging ideas from control theory, statistical signal processing, and machine learning. Taking an algorithmic approach, the book covers both classic and machine learningbased filtering algorithms. \u003c\/p\u003e\u003cp\u003eReaders of \u003ci\u003eNonlinear Filters\u003c\/i\u003e will greatly benefit from the wide spectrum of presented topics including stability, robustness, computability, and algorithmic sufficiency. Readers will also enjoy: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e Organization that allows the book to act as a stand-alone, self-contained reference\u003c\/li\u003e \u003cli\u003eA thorough exploration of the notion of observability, nonlinear observers, and the theory of optimal nonlinear filtering that bridges the gap between different science and engineering disciplines\u003c\/li\u003e \u003cli\u003eA profound account of Bayesian filters including Kalman filter and its variants as well as particle filter\u003c\/li\u003e \u003cli\u003eA rigorous derivation of the smooth variable structure filter as a predictor-corrector estimator formulated based on a stability theorem, used to confine the estimated states within a neighborhood of their true values\u003c\/li\u003e \u003cli\u003eA concise tutorial on deep learning and reinforcement learning\u003c\/li\u003e \u003cli\u003eA detailed expectation of the expectation maximization algorithm and its machine learning-based variants, used for joint state and parameter estimation\u003c\/li\u003e \u003cli\u003eGuidelines for constructing nonparametric Bayesian models from parametric ones\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for researchers, professors, and graduate students in engineering, computer science, applied mathematics, and artificial intelligence, \u003ci\u003eNonlinear Filters: Theory and Applications\u003c\/i\u003e will also earn a place in the libraries of those studying or practicing in fields involving pandemic diseases, cybersecurity, information fusion, augmented reality, autonomous driving, urban traffic network, navigation and tracking, robotics, power systems, hybrid technologies, and finance.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989695480037,"sku":"NP9781118835814","price":145.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118835814.jpg?v=1761785138","url":"https:\/\/k12savings.com\/es\/products\/nonlinear-filters-isbn-9781118835814","provider":"K12savings","version":"1.0","type":"link"}