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QRD-RLS Adaptive Filtering

José Antonio Jr Apolinário

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ISBN 10: 0387097333 / ISBN 13: 9780387097336
Published by Springer-Verlag Gmbh Mrz 2009, 2009
New Condition: Neu Buch
From Agrios-Buch (Bergisch Gladbach, Germany)

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About this Item

Neuware - I feel very honoured to have been asked to write a brief foreword for this book on QRD-RLS Adaptive Filtering-asubjectwhichhas been close to my heart for many years. The book is well written and very timely - I look forward personally to seeing it in print. The editor is to be congratulated on assembling such a highly esteemed team of contributing authors able to span the broad range of topics and concepts which underpin this subject. In many respects, and for reasons well expounded by the authors, the LMS al- rithm has reigned supreme since its inception, as the algorithm of choice for prac- cal applications of adaptive ltering. However, as a result of the relentless advances in electronic technology, the demand for stable and ef cient RLS algorithms is growing rapidly - not just because the higher computational load is no longer such a serious barrier, but also because the technological pull has grown much stronger in the modern commercial world of 3G mobile communications, cognitive radio, high speed imagery, and so on. 350 pp. Englisch. Bookseller Inventory # 9780387097336

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Bibliographic Details

Title: QRD-RLS Adaptive Filtering

Publisher: Springer-Verlag Gmbh Mrz 2009

Publication Date: 2009

Binding: Buch

Book Condition:Neu

About this title

Synopsis:

This book provides tools and knowledge in a simple way such that the reader is able to implement a particular QRD-RLS algorithm tailored for the application at hand. The material presents not only classical and recent research results but also the expertise of a number of authors that have contributed to the development of this fast converging class of adaptive filter algorithms. The intended audience encompasses graduate students, researchers, and practitioners working in the field of adaptive filtering or in need of using a robust and fast converging algorithm.

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