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  • Produktbild: Self-Organising Neural Networks
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Self-Organising Neural Networks Independent Component Analysis and Blind Source Separation

Aus der Reihe Perspectives in Neural Computing

97,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

25.06.1999

Abbildungen

IX, 271 p. 9 illus.

Verlag

Springer London

Seitenzahl

271

Maße (L/B/H)

23,5/15,5/1,6 cm

Gewicht

435 g

Auflage

1st Edition

Sprache

Englisch

ISBN

978-1-85233-066-8

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

25.06.1999

Abbildungen

IX, 271 p. 9 illus.

Verlag

Springer London

Seitenzahl

271

Maße (L/B/H)

23,5/15,5/1,6 cm

Gewicht

435 g

Auflage

1st Edition

Sprache

Englisch

ISBN

978-1-85233-066-8

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Self-Organising Neural Networks
  • Produktbild: Self-Organising Neural Networks
  • 1. Introduction.- 1.1 Self-Organisation and Blind Signal Processing.- 1.2 Outline of Book Chapters.- 2. Background to Blind Source Separation.- 2.1 Problem Formulation.- 2.2 Entropy and Information.- 2.3 A Contrast Function for ICA.- 2.4 Cumulant Expansions of Probability Densities and Higher Order Statistics.- 2.5 Gradient Based Function Optimisation.- 3. Fourth Order Cumulant Based Blind Source Separation.- 3.1 Early Algorithms and Techniques.- 3.2 The Method of Contrast Minimisation.- 3.3 Adaptive Source Separation Methods.- 3.4 Conclusions.- 4. Self-Organising Neural Networks.- 4.1 Linear Self-Organising Neural Networks.- 4.2 Non-Linear Self-Organising Neural Networks.- 4.3 Conclusions.- 5. The Non-Linear PCA Algorithm and Blind Source Separation.- 5.1 Introduction.- 5.2 Non-Linear PCA Algorithm and Source Separation.- 5.3 Non-Linear PCA Algorithm Cost Function.- 5.4 Non-Linear PCA Algorithm Activation Function.- 5.5 Conclusions.- 6. Non-Linear Feature Extraction and Blind Source Separation.- 6.1 Introduction.- 6.2 Structure Identification in Multivariate Data.- 6.3 Neural Network Implementation of Exploratory Projection Pursuit.- 6.4 Neural Exploratory Projection Pursuit and Blind Source Separation.- 6.5 Kurtosis Extrema.- 6.6 Finding Interesting and Independent Directions.- 6.7 Finding Multiple Interesting and Independent Directions Using Symmetric Feedback and Adaptive Whitening.- 6.8 Finding Multiple Interesting and Independent Directions Using Hierarchic Feedback and Adaptive Whitening.- 6.9 Simulations.- 6.10 Adaptive BSS Using a Deflationary EPP Network.- 6.11 Conclusions.- 7. Information Theoretic Non-Linear Feature Extraction And Blind Source Separation.- 7.1 Introduction.- 7.2 Information Theoretic Indices for EPP.- 7.3 Maximum Negentropy Learning.- 7.4 General Maximum Negentropy Learning.- 7.5 Stability Analysis of Generalised Algorithm.- 7.6 Simulation Results.- 7.7 Conclusions.- 8. Temporal Anti-Hebbian Learning.- 8.1 Introduction.- 8.2 Blind Source Separation of Convolutive Mixtures.- 8.3 Temporal Linear Anti-Hebbian Model.- 8.4 Comparative Simulation.- 8.5 Review of Existing Work on Adaptive Separation of Convolutive Mixtures.- 8.6 Maximum Likelihood Estimation and Source Separation.- 8.7 Temporal Anti-Hebbian Learning Based on Maximum Likelihood Estimation.- 8.8 Comparative Simulations Using Varying PDF Models.- 8.9 Conclusions.- 9. Applications.- 9.1 Introduction.- 9.2 Industrial Applications.- 9.3 Biomedical Applications.- 9.4 ICA: A Data Mining Tool.- 9.5 Experimental Results.- 9.6 Conclusions.- References.