1991
DOI: 10.1109/78.136559
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Fast adaptive RLS algorithms: a generalized inverse approach and analysis

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Cited by 15 publications
(6 citation statements)
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“…𝑒(𝑘) = 𝑦 𝑚𝑒𝑠 (𝑘) − 𝑦(𝑘) is the error between estimated and measured outputs. For the weight adaptation algorithm, we choose the recursive least squares (RLS) algorithm [21], [22]. It is a quadratic method which consists in minimizing a quadratic function of error e(k) between the measured signal and the model.…”
Section: Principle Of the Adaline Methodsmentioning
confidence: 99%
“…𝑒(𝑘) = 𝑦 𝑚𝑒𝑠 (𝑘) − 𝑦(𝑘) is the error between estimated and measured outputs. For the weight adaptation algorithm, we choose the recursive least squares (RLS) algorithm [21], [22]. It is a quadratic method which consists in minimizing a quadratic function of error e(k) between the measured signal and the model.…”
Section: Principle Of the Adaline Methodsmentioning
confidence: 99%
“…To estimate the best weight of the array (Godara, 1997a), efficient algorithms as LMS (Least Mean Squares) (Clarkson & White, 1987) and RLS (Recursive Least Squares) (Qiao, 1991) can be used. Due to this control over the radiation pattern envisaging a better management of the system, it is also possible to form dynamic cells using the multiple beams.…”
Section: Smart Receiversmentioning
confidence: 99%
“…The RLS algorithm is one of the most popular techniques [4], [5], [6]. It offers superior speed of convergence compared to LMS algorithm and its variations, especially in highly correlated environments.…”
Section: Introductionmentioning
confidence: 99%