Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Im Mittelpunkt dieses modernen und spezialisierten Bandes stehen adaptive Strukturen und unberwachte Lernalgorithmen, besonders im Hinblick auf effektive Computersimulationsprogramme. Anschauliche Illustrationen und viele Beispiele sowie eine interaktive CD-ROM ergnzen den Text. Preface. 1. Introduction to Blind Signal Processing: Problems and Applications. Problem formulations – An Overview. Potential Applications of Blind and Semi-Blind Signal Processing. 2. Solving a System of Algebraic Equations and Related Problems. Formulation of the Problem for Systems of Linear Equations. Least-Squares Problems. Least Absolute Deviation (1-norm) Solution of Systems of Linear Equations. Total Least-Squares and Data Least-Squares Problems. Sparse Signal Representation and Minimum Fuel Consumption Problem. 3. Principal/Minor Component Analysis and Related Problems. Introduction. Basic Properties of PCA. Extraction of Principal Components. Basic Cost Functions and Adaptive Algorithms for PCA. Robust PCA. Adaptive Learning Algorithms for MCA. Unified Parallel Algorithms for PCA/MCA and PSA/MSA. SVD in Relation to PCA and Matrix Subspaces. Multistage PCA for BSS. 4. Blind Decorrelation and SOS for Robust Blind Indentification. Spatial Decorrelation – Whitening Transforms. SOS Blind Identification Based on EVD. Improved Blind Identification Algorithms Based on EVD/SVD. Joint Diagonalization – Robust SOBI. Cancellation of Correlation. 5. Sequential Blind Signal Extraction. Introduction and Problem Formulation. Learning Algorithms Based on Kurtosis as Cost Function. On Line Algorithms for Blind Signal Extraction of Temporally Correlated Sources. Batch Algorithms for Blind Extraction of Temporally Correlated Sources. Statistical Approach to Sequential Extraction of Independent Sources. Statistical Approach to Temporally Correlated Sources. On-line Sequential Extraction of Convolved and Mixed Sources. Computer Simulation: Illustrative Examples. 6. Natural Gradient Approach to Independent Component Analysis. Basic Natural Gradient Algorithms. Generalizations of Basic Natural Gradient Algorithm. NG Algorithms for Blind Extraction. Generalized Gaussian Distribution Model. Natural Gradient Algorithms for Non-stationary Sources. 7. Locally Adaptive Algorithsm for ICA and their Implementations. Modified Jutten-Hrault Algorithms for Blind Separation of Sources. Iterative Matrix Inversion Approach to Derivation of Family of Robust ICA Algorithms. Computer Simulation. 8. Robust Techniques for BSS and ICA with Noisy Data. Introduction. Bias Removal Techniques for Prewhitening and ICA Algorithms. Blind Separation of Signals Buried in Additive Convolutive Reference Noise. Cumulants Based Adaptive ICA Algorithms. Robust Extraction of Arbitrary Group of Source Signals. Recurrent Neural Network Approach for Noise Cancellation. 9. Multichannel Blind Deconvolution – Natural Gradient Approach. SIMO Convolutive Models and Learning Algorithms for Estimation of Source Signal. Multichannel Blind Deconvolution with Constraints Imposed on FIR Filters. General Models for Multiple-Input Multiple-Output Blind Deconvolution. Relationships between BSS/ICA and MBD. Natural Gradient Algorithms with Nonholonomic Constraints. MBD of Non-minimum Phase System Using Filter Decomposition Approach. Computer Simulations Experiments. 10. Estimating Functions and Superefficiency for ICA and Deconvolution. Estimating Functions for Standard ICA. Estimating Functions in Noisy Case. Estimating Functions for Temporally Correlated Source Signals. Semiparametric Models for Multichannel Blind Deconvolution. Estimating Functions for MBD. 11. Blind Filtering and Separation Using State-Space Approach. Problem Formulation and Basic Models. Derivation of Basic Learning Algorithms. Estimation of Matrices [A,B] by Information Back-propagation. State Estimator – The Kalman Filter. Two-stage Separation Algorithm. 12. Nonlinear State Space Models – Semi-Blind Signal Processing. General Formulation of the Problem. Supervised – Unsupervised Learning Approach. References. Appendix A. Mathematical Preliminaries. Appendix B. Glossary of Symbols and Abbreviations. Index.