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ISBN: 0471607916
题名: Adaptive Blind Signal and Image Processing : Learning Algorithms and Applic
作者: Andrzej Cichocki
出版社: Wiley
页数: 586pp.
目录: 06Im Mittelpunkt dieses modernen und spezialisierten Bandes stehen adaptive Strukturen und unueberwachte Lernalgorithmen, besonders im Hinblick auf effektive Computersimulationsprogramme. Anschauliche Illustrationen und viele Beispiele sowie eine interaktive CD-ROM ergaenzen den Text. 04From the contents:1. Introduction to Blind Signal Processing: Problems and ApplicationsProblem formulations - OverviewPotential Applications of Blind and Semi-Blind Signal Processing2. Solving a System of Linear Equations and Related ProblemsFormulation of the Problem for Systems of Linear EquationsLeast-Squares ProblemsLeast Absolute Deviation (L_1-norm) Solution of Systems of Linear EquationsTotal Least-Squares and Data Least-Squares ProblemsMinimum Fuel Problem and Sparse Signal Representation3. Principal/Minor Component Analysis and Related ProblemsIntroductionBasic Properties of PCAExtraction of Principal ComponentsBasic Cost Functions and Adaptive Algorithms for PCARobust PCAAdaptive Learning Algorithms for MCAUnified Parallel Algorithms for PCA/MCA and PSA/MSASVD in Relation to PCA and Matrix SubspacesMultistage PCA for BSS4. Blind Decorrelation and Second Order Statistics for Blind IndentificationCancellation of CorrelationSpatia 04l Decorrelation - Whitening TransformsSOS Blind Identification Based on EVDImproved Blind Identification Algorithms Based on Multistage SVD/EVDJoint Diagonalization - Robust SOBI and JADE Algorithms5. Sequential Blind Signal ExtractionIntroduction and Problem FormulationLearning Algorithms Based on Kurtosis as Cost FunctionOn Line Algorithms for Blind Signal Extraction of Temporally Correlated SourcesBatch Algorithms for Blind Extraction of Temporally Correlated SourcesStatistical Approach to Sequential Extraction of Independent SourcesStatistical Approach to Temporally Correlated SourcesOn-line Sequential Extraction of Convolved and Mixed SourcesComputer Simulation: Illustrative Examples6. Natural Gradient Approach to Independent Component AnalysisBasic Natural Gradient AlgorithmsGeneralizations of Basic Natural Gradient AlgorithmNG Algorithms for Blind ExtractionNatural Gradient Algorithms for Overcomplete CaseGeneralized Gaussian Distribution Mod 04elNatural Gradient Algorithms for Non-stationary Sources7. Locally Adaptive Algorithsm for ICA and their ImplementationsModified Jutten-He rault Algorithms for Blind Separation of SourcesIterative Matrix Inversion Approach to Derivation of Family of Robust ICA AlgorithmsComputer Simulation Experiments8. Robust Techniques for BSS and ICA with Noisy DataIntroductionBias Removal Techniques for Prewhitening and ICA AlgorithmsBlind Separation of Signals Buried in Additive Convolutive Reference NoiseCumulants Based ICA Adaptive AlgorithmsRobust Extraction of Arbitrary Group of Source SignalsRecurrent Neural Network Approach for Noise Cancellation9. Multichannel Blind Deconvolution - Natural Gradient ApproachSIMO Convolutive Models and Learning Algorithms for Estimation of Source SignalMultichannel Blind Deconvolution with Constraitns Imposed on FIR FiltersGeneral Models for Multi Input Multi Output Blind Deconvolution ...
编目日期: 2005-4-6 0:00:00
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