An algorithm based on non-squared sum of the errors

In adaptive filtering, several algorithms are developed in the quest for greater convergence speed, mostly relying on second order statistics. Here we modify the Recursive Least Square (RLS) equations by using as performance surface a weighted sum of even error power. As a result, the equations turn...

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Bibliographic Details
Published inSignal processing Vol. 117; pp. 188 - 191
Main Authors da Silva, Cristiane, Barros, Allan Kardec, Santana, Ewaldo, de Araújo, Marcos A.F., Lopes, Marcus V. de S., Fonseca, João V., Principe, Jose C.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2015
Subjects
Adaptive filtering
Convergence speed
Non-quadratic function
RLS
RLS
Adaptive filtering
Non-quadratic function
Convergence speed
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Summary:In adaptive filtering, several algorithms are developed in the quest for greater convergence speed, mostly relying on second order statistics. Here we modify the Recursive Least Square (RLS) equations by using as performance surface a weighted sum of even error power. As a result, the equations turn out to be simple, elegant, while yielding faster convergence and preserving the computational cost when compared with the existing RLS algorithm. •In this study, we propose a new recursive algorithm that optimizes a sum of the even powers of the error.•In adaptive filtering, several algorithms were developed to improve convergence speed, but they rely mostly on second order statistics.•This algorithm seems to be faster than usual recursive algorithms presents in the literature.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2015.03.012