| Autore |
Haykin Simon S. <1931->
|
| Edizione | [Fifth edition, International edition.] |
| Pubbl/distr/stampa |
Upper Saddle River : , : Pearson, , [2014]
|
| Descrizione fisica |
1 online resource (912 pages) : illustrations (some color)
|
| Disciplina |
621.3815324
|
| Collana |
Always learning
|
| Soggetto topico |
Adaptive filters
|
| ISBN |
0-273-77572-3
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Cover -- Title -- 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 -- 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 Proceses -- 1.12 Power Spectral Density -- 1.13 Propert ies of Power Spectral Density -- 1.14 Transmission of a Stationary Process Through a Linear Filter -- 1.15 Cramér 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 -- 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 -- 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 -- 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 Viewed as a Deterministic Search Method -- 4.6 Virtue and Limitation of the Steepest-Descent Algorithm -- 4.7 Summary and Discussion -- Problems -- Chapter 5 Method of Stochastic Gradient Descent -- 5.1 Principles of Stochastic Gradient Descent -- 5.2 Application 1: Least-Mean-Square (LMS) Algorithm -- 5.3 Application 2: Gradient-Adaptive Lattice Filtering Algorithm -- 5.4 Other Applications of Stochastic Gradient Descent -- 5.5 Summary and Discussion -- Problems -- 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 a Minimum-Variance Distortionless-Response Beamformer -- 6.10 Summary and Discussion -- Problems -- 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 -- 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 -- Chapter 9 Method of Least-Squares -- 9.1 Statement of the Linear Least-Squares Estimation Problem -- 9.2 Data Windowing -- 9.3 Principle of Orthogonality Revisited -- 9.4 Minimum Sum of Error Squares -- 9.5 Normal Equations and Linear Least-Squares Filters -- 9.6 Time-Average Correlation Matrix Φ -- 9.7 Reformulation of the Normal Equations in Terms of Data Matrices -- 9.8 Properties of Least-Squares Estimates -- 9.9 Minimum-Variance Distortionless Response (MVDR) Spectrum Estimation -- 9.10 Regularized MVDR Beamforming -- 9.11 Singular-Value Decomposition -- 9.12 Pseudoinverse -- 9.13 Interpretation of Singular Values and Singular Vectors -- 9.14 Minimum-Norm Solution to the Linear Least-Squares Problem -- 9.15 Normalized LMS Algorithm Viewed as the Minimum-Norm Solution to an Underdetermined Least-Squares Estimation Problem -- 9.16 Summary and Discussion -- Problems -- Chapter 10 The Recursive Least-Squares (RLS) Algorithm -- 10.1 Some Preliminaries -- 10.2 The Matrix Inversion Lemma -- 10.3 The Exponentially Weighted RLS Algorithm -- 10.4 Selection of the Regularization Parameter -- 10.5 Updated Recursion for the Sum of Weighted Error Squares -- 10.6 Example: Single-Weight Adaptive Noise Canceller -- 10.7 Statistical Learning Theory -- 10.8 Efficiency -- 10.9 Computer Experiment on Adaptive Equalization -- 10.10 Summary and Discussion -- Problems -- Chapter 11 Robustness -- 11.1 Robustness, Adaptation, and Disturbances.
11.2 Robustness: Preliminary Considerations Rooted in H∞ Optimization -- 11.3 Robustness of the LMS Algorithm -- 11.4 Robustness of the RLS Algorithm -- 11.5 Comparative Evaluations of the LMS and RLS Algorithms from the Perspective of Robustness -- 11.6 Risk-Sensitive Optimality -- 11.7 Trade-Offs Between Robustness and Efficiency -- 11.8 Summary and Discussion -- Problems -- Chapter 12 Finite-Precision Effects -- 12.1 Quantization Errors -- 12.2 Least-Mean-Square (LMS) Algorithm -- 12.3 Recursive Least-Squares (RLS) Algorithm -- 12.4 Summary and Discussion -- Problems -- Chapter 13 Adaptation in Nonstationary Environments -- 13.1 Causes and Consequences of Nonstationarity -- 13.2 The System Identification Problem -- 13.3 Degree of Nonstationarity -- 13.4 Criteria for Tracking Assessment -- 13.5 Tracking Performance of the LMS Algorithm -- 13.6 Tracking Performance of the RLS Algorithm -- 13.7 Comparison of the Tracking Performance of LMS and RLS Algorithms -- 13.8 Tuning of Adaptation Parameters -- 13.9 Incremental Delta-Bar-Delta (IDBD) Algorithm -- 13.10 Autostep Method -- 13.11 Computer Experiment: Mixture of Stationary and Nonstationary Environmental Data -- 13.12 Summary and Discussion -- Problems -- Chapter 14 Kalman Filters -- 14.1 Recursive Minimum Mean-Square Estimation for Scalar Random Variables -- 14.2 Statement of the Kalman Filtering Problem -- 14.3 The Innovations Process -- 14.4 Estimation of the State Using the Innovations Process -- 14.5 Filtering -- 14.6 Initial Conditions -- 14.7 Summary of the Kalman Filter -- 14.8 Optimality Criteria for Kalman Filtering -- 14.9 Kalman Filter as the Unifying Basis for RLS Algorithms -- 14.10 Covariance Filtering Algorithm -- 14.11 Information Filtering Algorithm -- 14.12 Summary and Discussion -- Problems -- Chapter 15 Square-Root Adaptive Filtering Algorithms.
15.1 Square-Root Kalman Filters -- 15.2 Building Square-Root Adaptive Filters on the Two Kalman Filter Variants -- 15.3 QRD-RLS Algorithm -- 15.4 Adaptive Beamforming -- 15.5 Inverse QRD-RLS Algorithm -- 15.6 Finite-Precision Effects -- 15.7 Summary and Discussion -- Problems -- Chapter 16 Order-Recursive Adaptive Filtering Algorithm -- 16.1 Order-Recursive Adaptive Filters Using Least-Squares Estimation: An Overview -- 16.2 Adaptive Forward Linear Prediction -- 16.3 Adaptive Backward Linear Prediction -- 16.4 Conversion Factor -- 16.5 Least-Squares Lattice (LSL) Predictor -- 16.6 Angle-Normalized Estimation Errors -- 16.7 First-Order State-Space Models for Lattice Filtering -- 16.8 QR-Decomposition-Based Least-Squares Lattice (QRD-LSL) Filters -- 16.9 Fundamental Properties of the QRD-LSL Filter -- 16.10 Computer Experiment on Adaptive Equalization -- 16.11 Recursive (LSL) Filters Using A Posteriori Estimation Errors -- 16.12 Recursive LSL Filters Using A Priori Estimation Errors with Error Feedback -- 16.13 Relation Between Recursive LSL and RLS Algorithms -- 16.14 Finite-Precision Effects -- 16.15 Summary and Discussion -- Problems -- Chapter 17 Blind Deconvolution -- 17.1 Overview of Blind Deconvolution -- 17.2 Channel Identifiability Using Cyclostationary Statistics -- 17.3 Subspace Decomposition for Fractionally Spaced Blind Identification -- 17.4 Bussgang Algorithm for Blind Equalization -- 17.5 Extension of the Bussgang Algorithm to Complex Baseband Channels -- 17.6 Special Cases of the Bussgang Algorithm -- 17.7 Fractionally Spaced Bussgang Equalizers -- 17.8 Estimation of Unknown Probability Distribution Function of Signal Source -- 17.9 Summary and Discussion -- Problems -- Epilogue -- 1. Robustness, Efficiency, and Complexity -- 2. Kernel-Based Nonlinear Adaptive Filtering -- Appendix A Theory of Complex Variables.
A.1 Cauchy-Riemann Equations.
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| Record Nr. | UNINA-9910150209303321 |
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Adaptive digital filters / / Branko Kovacevic, Zoran Banjac, Milan Milosavljevic
