Andrzej Cichocki, Shun-Ichi Amari

Adaptive Blind Signal and Image Processing

Learning Algorithms and Applications

• Gebundenes Buch

Produktbeschreibung

Im Mittelpunkt dieses modernen und spezialisierten Bandes stehen adaptive Strukturen und unüberwachte Lernalgorithmen, besonders im Hinblick auf effektive Computersimulationsprogramme. Anschauliche Illustrationen und viele Beispiele sowie eine interaktive CD-ROM ergänzen den Text.

Produktdetails

• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 586
• Erscheinungstermin: 14. Juni 2002
• Englisch
• Abmessung: 250mm x 175mm x 36mm
• Gewicht: 1142g
• ISBN-13: 9780471607915
• ISBN-10: 0471607916
• Artikelnr.: 09695820

Autorenporträt

Andrzej Cichocki received the M.Sc. (with honors), Ph.D. and Dr.Sc. (Habilitation) degrees, all in electrical engineering, from Warsaw University of Technology in Poland. Since 1972, he has been with the Institute of Theory of Electrical Engineering, Measurement and Information Systems, Faculty of Electrical Engineering at the Warsaw University of Technology, where he obtain a title of a full Professor in 1995. He spent several years at University Erlangen-Nuerenberg in Germany, at the Chair of Applied and Theoretical Electrical Engineering directed by Professor Rolf Unbehauen, as an Alexander-von-Humboldt Research Fellow and Guest Professor. In 1995-1997 he was a team leader of the laboratory for Artificial Brain Systems, at Frontier Research Program RIKEN (Japan), in the Brain Information Processing Group.

Inhaltsangabe

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-Hérault 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.