Tài liệu Kalman Filtering and Neural Networks - Contents pptx

KALMAN FILTERING AND
NEURAL NETWORKS
Kalman Filtering and Neural Networks, Edited by Simon Haykin
Copyright # 2001 John Wiley & Sons, Inc.
ISBNs: 0-471-36998-5 (Hardback); 0-471-22154-6 (Electronic)
KALMAN FILTERING AND
NEURAL NETWORKS
Edited by
Simon Haykin
Communications Research Laboratory,
McMaster University, Hamilton, Ontario, Canada
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CONTENTS
Preface xi
Contributors xiii
1 Kalman Filters 1
Simon Haykin
1.1 Introduction = 1
1.2 Optimum Estimates = 3
1.3 Kalman Filter = 5
1.4 Divergence Phenomenon: Square-Root Filtering = 10
1.5 Rauch–Tung–Striebel Smoother = 11
1.6 Extended Kalman Filter = 16
1.7 Summary = 20
References = 20
2 Parameter-Based Kalman Filter Training:
Theory and Implementation 23
Gintaras V. Puskorius and Lee A. Feldkamp
2.1 Introduction = 23
2.2 Network Architectures = 26
2.3 The EKF Procedure = 28
2.3.1 Global EKF Training = 29
2.3.2 Learning Rate and Scaled Cost Function = 31
2.3.3 Parameter Settings = 32
2.4 Decoupled EKF (DEKF) = 33
2.5 Multistream Training = 35

v
2.5.1 Some Insight into the Multistream Technique = 40
2.5.2 Advantages and Extensions of Multistream
Training = 42
2.6 Computational Considerations = 43
2.6.1 Derivative Calculations = 43
2.6.2 Computationally Efficient Formulations for
Multiple-Output Problems = 45
2.6.3 Avoiding Matrix Inversions = 46
2.6.4 Square-Root Filtering = 48
2.7 Other Extensions and Enhancements = 51
2.7.1 EKF Training with Constrained Weights = 51
2.7.2 EKF Training with an Entropic Cost Function = 54
2.7.3 EKF Training with Scalar Errors = 55
2.8 Automotive Applications of EKF Training = 57
2.8.1 Air=Fuel Ratio Control = 58
2.8.2 Idle Speed Control = 59
2.8.3 Sensor-Catalyst Modeling = 60
2.8.4 Engine Misfire Detection = 61
2.8.5 Vehicle Emissions Estimation = 62
2.9 Discussion = 63
2.9.1 Virtues of EKF Training = 63
2.9.2 Limitations of EKF Training = 64
2.9.3 Guidelines for Implementation and Use = 64
References = 65
3 Learning Shape and Motion from Image Sequences 69
Gaurav S. Patel, Sue Becker, and Ron Racine
3.1 Introduction = 69
3.2 Neurobiological and Perceptual Foundations of our Model = 70
3.3 Network Description = 71

3.4 Experiment 1 = 73
3.5 Experiment 2 = 74
3.6 Experiment 3 = 76
3.7 Discussion = 77
References = 81
vi
CONTENTS
4 Chaotic Dynamics 83
Gaurav S. Patel and Simon Haykin
4.1 Introduction = 83
4.2 Chaotic (Dynamic) Invariants = 84
4.3 Dynamic Reconstruction = 85
4.4 Modeling Numerically Generated Chaotic Time Series = 87
4.4.1 Logistic Map = 87
4.4.2 Ikeda Map = 91
4.4.3 Lorenz Attractor = 99
4.5 Nonlinear Dynamic Modeling of Real-World
Time Series = 106
4.5.1 Laser Intensity Pulsations = 106
4.5.2 Sea Clutter Data = 113
4.6 Discussion = 119
References = 121
5 Dual Extended Kalman Filter Methods 123
Eric A. Wan and Alex T. Nelson
5.1 Introduction = 123
5.2 Dual EKF – Prediction Error = 126
5.2.1 EKF – State Estimation = 127
5.2.2 EKF – Weight Estimation = 128
5.2.3 Dual Estimation = 130
5.3 A Probabilistic Perspective = 135

5.3.1 Joint Estimation Methods = 137
5.3.2 Marginal Estimation Methods = 140
5.3.3 Dual EKF Algorithms = 144
5.3.4 Joint EKF = 149
5.4 Dual EKF Variance Estimation = 149
5.5 Applications = 153
5.5.1 Noisy Time-Series Estimation and Prediction = 153
5.5.2 Economic Forecasting – Index of Industrial
Production = 155
5.5.3 Speech Enhancement = 157
5.6 Conclusions = 163
Acknowledgments = 164
CONTENTS
vii