Adaptive Filters Theory and Applications.

Detalles Bibliográficos
Autor principal: Farhang-Boroujeny, Behrouz
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 1999.
Colección:New York Academy of Sciences Ser.
Temas:
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Preface
  • Acknowledgments
  • Chapter 1 Introduction
  • 1.1 Linear Filters
  • 1.2 Adaptive Filters
  • 1.3 Adaptive Filter Structures
  • 1.4 Adaptation Approaches
  • 1.4.1 Approach Based on Wiener Filter Theory
  • 1.4.2 Method of Least-Squares
  • 1.5 Real and Complex Forms of Adaptive Filters
  • 1.6 Applications
  • 1.6.1 Modeling
  • 1.6.2 Inverse Modeling
  • 1.6.3 Linear Prediction
  • 1.6.4 Interference Cancellation
  • Chapter 2 Discrete-Time Signals and Systems
  • 2.1 Sequences and z-Transform
  • 2.2 Parseval's Relation
  • 2.3 System Function
  • 2.4 Stochastic Processes
  • 2.4.1 Stochastic Averages
  • 2.4.2 z-Transform Representations
  • 2.4.3 The Power Spectral Density
  • 2.4.4 Response of Linear Systems to Stochastic Processes
  • 2.4.5 Ergodicity and Time Averages
  • Problems
  • Chapter 3 Wiener Filters
  • 3.1 Mean-Squared Error Criterion
  • 3.2 Wiener Filter-Transversal, Real-Valued Case
  • 3.3 Principle of Orthogonality
  • 3.4 Normalized Performance Function
  • 3.5 Extension to Complex-Valued Case
  • 3.6 Unconstrained Wiener Filters
  • 3.6.1 Performance Function
  • 3.6.2 Optimum Transfer Function
  • 3.6.3 Modeling
  • 3.6.4 Inverse Modeling
  • 3.6.5 Noise Cancellation
  • 3.7 Summary and Discussion
  • Problems
  • Chapter 4 Eigenanalysis and Performance Surface
  • 4.1 Eigenvalues and Eigenvectors
  • 4.2 Properties of Eigenvalues and Eigenvectors
  • 4.3 Performance Surface
  • Problems
  • Chapter 5 Search Methods
  • 5.1 Method of Steepest Descent
  • 5.2 Learning Curve
  • 5.3 Effect of Eigenvalue Spread
  • 5.4 Newton's Method
  • 5.5 An Alternative Interpretation of Newton's Algorithm
  • Problems
  • Chapter 6 LMS Algorithm
  • 6.1 Derivation of LMS Algorithm
  • 6.2 Average Tap-Weight Behavior of the LMS Algorithm
  • 6.3 MSE Behavior of the LMS Algorithm
  • 6.3.1 Learning Curve
  • 6.3.2 Weight-Error Correlation Matrix
  • 6.3.3 Excess MSE and Misadjustment
  • 6.3.4 Stability
  • 6.3.5 The Effect of Initial Values of Tap Weights on the Transient Behavior of the LMS Algorithm
  • 6.4 Computer Simulations
  • 6.4.1 System Modeling
  • 6.4.2 Channel Equalization
  • 6.4.3 Adaptive Line Enhancement
  • 6.4.4 Beamforming
  • 6.5 Simplified LMS Algorithms
  • 6.6 Normalized LMS Algorithm
  • 6.7 Affine Projection LMS Algorithm
  • 6.8 Variable Step-Size LMS Algorithm
  • 6.9 LMS Algorithm for Complex-Valued Signals
  • 6.10 Beamforming (Revisited)
  • 6.11 Linearly Constrained LMS Algorithm
  • 6.11.1 Statement of the Problem and Its Optimal Solution
  • 6.11.2 Update Equations
  • 6.11.3 Extension to the Complex-Valued Case
  • Problems
  • Chapter 7 Transform Domain Adaptive Filters
  • 7.1 Overview of Transform Domain Adaptive Filters
  • 7.2 Band-Partitioning Property of Orthogonal Transforms
  • 7.3 Orthogonalization Property of Orthogonal Transforms
  • 7.4 Transform Domain LMS Algorithm
  • 7.5 Ideal LMS-Newton Algorithm and Its Relationship with TDLMS
  • 7.6 Selection of the Transform T

Ejemplares similares