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Advanced Concepts in Adaptive Signal Processing

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Table of Contents

Preface. 1: Introduction and Background. 1.1. Common Adaptive Concepts from Different Disciplines. 1.2. Generic Applications of Adaptive Methods. 1.3. Performance Measures in Adaptive Systems. 1.4. The Minimum Mean Squared Error Solution. 1.5. Adaptive Algorithms for FIR Systems. 1.6. Adaptive Algorithms for IIR Systems. 1.7. New Horizons in Adaptive Signal Processing. 1.8. Notation and Conventions. 2: Advanced Algorithms for 1-D Adaptive Filtering. 2.2. Data- Reusing LMS Algorithms. 2.3. Orthogonalization by PR Modulation. 2.3. The Gauss-Newton Adaptive Filtering algorithm. 2.4. Block Adaptive IIR Filters Using the PCG Method. 3: Structures and Algorithms for Two-Dimensional Adaptive Signal Processing. 3.1. Applications of Two-Dimensional Adaptive Filtering. 3.2. Two- Dimensional FIR Adaptive Filtering. 3.3. Two-Dimensional IIR Adaptive Filters. 3.4. Two-Dimensional IIR Adaptive Filtering Experiments. 3.5. Uniqueness Characteristics of the 2-D IIR MSE Minimization. 4: Adaptive Fault Tolerance. 4.1. Application of AFT to FIR Adaptive Filters. 4.2. Adaptive Filter Structures. 4.3. A Simple Fault Tolerant FIR Adaptive Filter. 4.4. The Transform Domain FTAF. 4.5. The DFT-Based TDFTAF with the Conjugate Gradient. 4.6. Robust and Practical TDFTAFs. 4.7. Full Fault Tolerance Transforms. 4.8. Discussion. 5: Adaptive Polynomial Filters. 5.1. The Volterra Series. 5.2. Gradient Based Adaptive Volterra Filters. 5.3. RLSSecond-Order Volterra Adaptive Filter. 5.4. LS Lattice Second-Order Volterra Adaptive Filter. 5.5. QR-Based LS Lattice Second Order Volterra Filter. 5.6. The Adaptive Volterra Filter for Gaussian Signals. 5.7. Other Polynomial-Based Nonlinear Adaptive Filters. 5.8. Discussion. Appendix. Subject Index.

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