05396nam 2200649Ia 450 991083075200332120230124181247.01-280-36756-397866103675660-470-31226-20-471-46421-X0-471-22154-6(CKB)111056485580890(EBL)152678(OCoLC)475872145(SSID)ssj0000080453(PQKBManifestationID)11125840(PQKBTitleCode)TC0000080453(PQKBWorkID)10095355(PQKB)11781840(MiAaPQ)EBC152678(EXLCZ)9911105648558089020010720d2001 uy 0engur|n|---|||||txtccrKalman filtering and neural networks[electronic resource] /edited by Simon HaykinNew York Wileyc20011 online resource (302 p.)Adaptive and learning systems for signal processing, communications, and controlDescription based upon print version of record.0-471-36998-5 Includes bibliographical references and index.KALMAN FILTERING AND NEURAL NETWORKS; CONTENTS; Preface; Contributors; 1 Kalman Filters; 1.1 Introduction; 1.2 Optimum Estimates; 1.3 Kalman Filter; 1.4 Divergence Phenomenon: Square-Root Filtering; 1.5 Rauch-Tung-Striebel Smoother; 1.6 Extended Kalman Filter; 1.7 Summary; References; 2 Parameter-Based Kalman Filter Training: Theory and Implementation; 2.1 Introduction; 2.2 Network Architectures; 2.3 The EKF Procedure; 2.3.1 Global EKF Training; 2.3.2 Learning Rate and Scaled Cost Function; 2.3.3 Parameter Settings; 2.4 Decoupled EKF (DEKF); 2.5 Multistream Training2.5.1 Some Insight into the Multistream Technique2.5.2 Advantages and Extensions of Multistream Training; 2.6 Computational Considerations; 2.6.1 Derivative Calculations; 2.6.2 Computationally Efficient Formulations for Multiple-Output Problems; 2.6.3 Avoiding Matrix Inversions; 2.6.4 Square-Root Filtering; 2.7 Other Extensions and Enhancements; 2.7.1 EKF Training with Constrained Weights; 2.7.2 EKF Training with an Entropic Cost Function; 2.7.3 EKF Training with Scalar Errors; 2.8 Automotive Applications of EKF Training; 2.8.1 Air/Fuel Ratio Control; 2.8.2 Idle Speed Control2.8.3 Sensor-Catalyst Modeling2.8.4 Engine Misfire Detection; 2.8.5 Vehicle Emissions Estimation; 2.9 Discussion; 2.9.1 Virtues of EKF Training; 2.9.2 Limitations of EKF Training; 2.9.3 Guidelines for Implementation and Use; References; 3 Learning Shape and Motion from Image Sequences; 3.1 Introduction; 3.2 Neurobiological and Perceptual Foundations of our Model; 3.3 Network Description; 3.4 Experiment 1; 3.5 Experiment 2; 3.6 Experiment 3; 3.7 Discussion; References; 4 Chaotic Dynamics; 4.1 Introduction; 4.2 Chaotic (Dynamic) Invariants; 4.3 Dynamic Reconstruction4.4 Modeling Numerically Generated Chaotic Time Series4.4.1 Logistic Map; 4.4.2 Ikeda Map; 4.4.3 Lorenz Attractor; 4.5 Nonlinear Dynamic Modeling of Real-World Time Series; 4.5.1 Laser Intensity Pulsations; 4.5.2 Sea Clutter Data; 4.6 Discussion; References; 5 Dual Extended Kalman Filter Methods; 5.1 Introduction; 5.2 Dual EKF-Prediction Error; 5.2.1 EKF-State Estimation; 5.2.2 EKF-Weight Estimation; 5.2.3 Dual Estimation; 5.3 A Probabilistic Perspective; 5.3.1 Joint Estimation Methods; 5.3.2 Marginal Estimation Methods; 5.3.3 Dual EKF Algorithms; 5.3.4 Joint EKF5.4 Dual EKF Variance Estimation5.5 Applications; 5.5.1 Noisy Time-Series Estimation and Prediction; 5.5.2 Economic Forecasting-Index of Industrial Production; 5.5.3 Speech Enhancement; 5.6 Conclusions; Acknowledgments; Appendix A: Recurrent Derivative of the Kalman Gain; Appendix B: Dual EKF with Colored Measurement Noise; References; 6 Learning Nonlinear Dynamical System Using the Expectation-Maximization Algorithm; 6.1 Learning Stochastic Nonlinear Dynamics; 6.1.1 State Inference and Model Learning; 6.1.2 The Kalman Filter; 6.1.3 The EM Algorithm; 6.2 Combining EKS and EM6.2.1 Extended Kalman Smoothing (E-step)State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. OAdaptive and learning systems for signal processing, communications, and control.Kalman filteringNeural networks (Computer science)Kalman filtering.Neural networks (Computer science)006.3/2621.3815324Haykin Simon S.1931-8857MiAaPQMiAaPQMiAaPQBOOK9910830752003321Kalman filtering and neural networks4065234UNINA