The Signed Regressor Least Mean Fourth (SRLMF) Adaptive Algorithm | Analysis and Design

Mohammed Mujahid Ulla Faiz

ISBN 10: 363926262X / ISBN 13: 9783639262629

Published by VDM Verlag Dr. Müller, 2010

About this Item

The Signed Regressor Least Mean Fourth (SRLMF) Adaptive Algorithm | Analysis and Design | Mohammed Mujahid Ulla Faiz | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639262629 | Verantwortliche Person für die EU: VDM Verlag Dr. Müller, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Seller Inventory # 101046093

Bibliographic Details

• Title: The Signed Regressor Least Mean Fourth (SRLMF) Adaptive Algorithm | Analysis and Design
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2010
• Language: English
• ISBN 10: 363926262X
• ISBN 13: 9783639262629
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 5W x 150H x 220D millimeters 5 millimeters width by 150 millimeters height by 220 millimeters depth
• Weight: 129 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

In this book, a novel algorithm, called the signed regressor least mean fourth (SRLMF) adaptive algorithm, that reduces the computational cost and complexity while maintaining good performance is presented. Expressions are derived for the steady-state excess-mean-square error (EMSE) of the SRLMF algorithm in a stationary environment. Also, expressions are obtained for the tracking EMSE of the SRLMF algorithm in a non-stationary environment. An optimum value of the step-size is also derived. Moreover, the weighted variance relation has been extended in order to derive expressions for the mean-square error (MSE) and the mean-square deviation (MSD) of the proposed algorithm during the transient phase. Computer simulations are carried out to corroborate the theoretical findings. It is shown that there is a good match between the theoretical and simulated results. It is also shown that the SRLMF algorithm has no performance degradation when compared with the least mean fourth (LMF) algorithm. The results in this study emphasize the usefulness of this algorithm in applications requiring reduced implementation costs for which the LMF algorithm is too complex.