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Adaptive Filter Theory (International ed of 5th revised ed)
By: Simon S. Haykin (author)PaperbackOnly 1 in stock
Description
For courses in Adaptive Filters. Haykin examines both the mathematical theory behind various linear adaptive filters and the elements of supervised multilayer perceptrons. In its fifth edition, this highly successful book has been updated and refined to stay current with the field and develop concepts in as unified and accessible a manner as possible.Create a review
Contents
Preface Acknowledgments Background and Preview 1. The Filtering Problem 2. Linear Optimum Filters 3. Adaptive Filters 4. Linear Filter Structures 5. Approaches to the Development of Linear Adaptive Filters 6. Adaptive Beamforming 7. Four Classes of Applications 8. Historical Notes Bibliography Chapter 1 Stochastic Processes and Models 1.1 Partial Characterization of a Discrete-Time Stochastic Process 1.2 Mean Ergodic Theorem 1.3 Correlation Matrix 1.4 Correlation Matrix of Sine Wave Plus Noise 1.5 Stochastic Models 1.6 Wold Decomposition 1.7 Asymptotic Stationarity of an Autoregressive Process 1.8 Yule-Walker Equations 1.9 Computer Experiment: Autoregressive Process of Order Two 1.10 Selecting the Model Order 1.11 Complex Gaussian Processes 1.12 Power Spectral Density 1.13 Properties of Spectral Density 1.14 Transmission of a Stationary Process Through a Linear Filter 1.15 Cramer Spectral Representation for a Stationary Process 1.16 Power Spectrum Estimation 1.17 Other Statistical Characteristics of a Stochastic Process 1.18 Polyspectra 1.19 Spectral-Correlation Density 1.20 Summary and Discussion Problems Bibliography Chapter 2 Wiener Filters 2.1 Linear Optimum Filtering: Statement of the Problem 2.2 Principle of Orthogonality 2.3 Minimum Mean-Square Error 2.4 Wiener-Hopf Equations 2.5 Error-Performance Surface 2.6 Multiple Linear Regression Model 2.7 Example 2.8 Linearly Constrained Minimum-Variance Filter 2.9 Generalized Sidelobe Cancellers 2.10 Summary and Discussion Problems Bibliography Chapter 3 Linear Prediction 3.1 Forward Linear Prediction 3.2 Backward Linear Prediction 3.3 Levinson-Durbin Algorithm 3.4 Properties of Prediction-Error Filters 3.5 Schur-Cohn Test 3.6 Autoregressive Modeling of a Stationary Stochastic Process 3.7 Cholesky Factorization 3.8 Lattice Predictors 3.9 All-Pole, All-Pass Lattice Filter 3.10 Joint-Process Estimation 3.11 Predictive Modeling of Speech 3.12 Summary and Discussion Problems Bibliography Chapter 4 Method of Steepest Descent 4.1 Basic Idea of the Steepest-Descent Algorithm 4.2 The Steepest-Descent Algorithm Applied to the Wiener Filter 4.3 Stability of the Steepest-Descent Algorithm 4.4 Example 4.5 The Steepest-Descent Algorithm as a Deterministic Search Method 4.6 Virtue and Limitation of the Steepest-Descent Algorithm 4.7 Summary and Discussion Problems Bibliography Chapter 5 Method of Stochastic Gradient Descent 5.1 Principles of Stochastic Gradient Descent 5.2 Application: Least-Mean-Square (LMS) Algorithm 5.3 Gradient-Adaptive Lattice Filtering Algorithm 5.4 Other Applications of Stochastic Gradient Descent 5.5 Summary and Discussion Problems Bibliography Chapter 6 The Least-Mean-Square (LMS) Algorithm 6.1 Signal-Flow Graph 6.2 Optimality Considerations 6.3 Applications 6.4 Statistical Learning Theory 6.5 Transient Behavior and Convergence Considerations 6.6 Efficiency 6.7 Computer Experiment on Adaptive Prediction 6.8 Computer Experiment on Adaptive Equalization 6.9 Computer Experiment on Minimum-Variance Distortionless-Response Beamformer 6.10 Summary and Discussion Problems Bibliography Chapter 7 Normalized Least-Mean-Square (LMS) Algorithm and Its Generalization 7.1 Normalized LMS Algorithm: The Solution to a Constrained Optimization Problem 7.2 Stability of the Normalized LMS Algorithm 7.3 Step-Size Control for Acoustic Echo Cancellation 7.4 Geometric Considerations Pertaining to the Convergence Process for Real-Valued Data 7.5 Affine Projection Adaptive Filters 7.6 Summary and Discussion Problems Bibliography Chapter 8 Block-Adaptive Filters 8.1 Block-Adaptive Filters: Basic Ideas 8.2 Fast Block LMS Algorithm 8.3 Unconstrained Frequency-Domain Adaptive Filters 8.4 Self-Orthogonalizing Adaptive Filters 8.5 Computer Experiment on Adaptive Equalization 8.6 Subband Adaptive Filters 8.7 Summary and Discussion Problems Bibliography Chapter 9 Method of Least Squares 9.1 Statement of the Linear Least-Squares Estimation Problem 9.2 Data Windowing 9.3 Principle of OrthogonalityProduct Details
- publication date: 26/07/2013
- ISBN13: 9780273764083
- Format: Paperback
- Number Of Pages: 912
- ID: 9780273764083
- weight: 1337
- ISBN10: 027376408X
- edition: International ed of 5th revised ed
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- 1st Class Delivery: Yes
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