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Nonlinear Blind Source Separation and Blind Mixture Identification

- Methods for Bilinear, Linear-quadratic and Polynomial Mixtures
Af: Shahram Hosseini, Leonardo Tomazeli Duarte, Yannick Deville Engelsk Paperback

Nonlinear Blind Source Separation and Blind Mixture Identification

- Methods for Bilinear, Linear-quadratic and Polynomial Mixtures
Af: Shahram Hosseini, Leonardo Tomazeli Duarte, Yannick Deville Engelsk Paperback
Tjek vores konkurrenters priser
This book provides a detailed survey of the methods that were recently developed to handle advanced versions of the blind source separation problem, which involve several types of nonlinear mixtures. Another attractive feature of the book is that it is based on a coherent framework. More precisely, the authors first present a general procedure for developing blind source separation methods. Then, all reported methods are defined with respect to this procedure. This allows the reader not only to more easily follow the description of each method but also to see how these methods relate to one another. The coherence of this book also results from the fact that the same notations are used throughout the chapters for the quantities (source signals and so on) that are used in various methods. Finally, among the quite varied types of processing methods that are presented in this book, a significant part of this description is dedicated to methods based on artificial neural networks, especially recurrent ones, which are currently of high interest to the data analysis and machine learning community in general, beyond the more specific signal processing and blind source separation communities.

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This book provides a detailed survey of the methods that were recently developed to handle advanced versions of the blind source separation problem, which involve several types of nonlinear mixtures. Another attractive feature of the book is that it is based on a coherent framework. More precisely, the authors first present a general procedure for developing blind source separation methods. Then, all reported methods are defined with respect to this procedure. This allows the reader not only to more easily follow the description of each method but also to see how these methods relate to one another. The coherence of this book also results from the fact that the same notations are used throughout the chapters for the quantities (source signals and so on) that are used in various methods. Finally, among the quite varied types of processing methods that are presented in this book, a significant part of this description is dedicated to methods based on artificial neural networks, especially recurrent ones, which are currently of high interest to the data analysis and machine learning community in general, beyond the more specific signal processing and blind source separation communities.

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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 71
ISBN-13: 9783030649760
Indbinding: Paperback
Udgave:
ISBN-10: 3030649768
Kategori: Numerisk analyse
Udg. Dato: 3 feb 2021
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 3 feb 2021
Forfatter(e): Shahram Hosseini, Leonardo Tomazeli Duarte, Yannick Deville
Forfatter(e) Shahram Hosseini, Leonardo Tomazeli Duarte, Yannick Deville

Kategori Numerisk analyse

ISBN-13 9783030649760

Sprog Engelsk

Indbinding Paperback

Sider 71

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 3 feb 2021

Oplagsdato 3 feb 2021

Forlag Springer Nature Switzerland AG

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