... In this paper, the geometric approach used in [ 7 , 8 ] and [2] is extended to the derivation of a fast pole-zero (ARMA) Recursive Least Squares algorithmo The work is also an extension [r] ...

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... logic-based **recursive** **least** **squares** filter (FLRLSF) is presented for maneuvering target tracking (MTT) in situations of observations with unknown random ...standard **recursive** **least** ...

9

... adaptive **recursive** **least** **squares** filter applied to time series derived from emergency department (ED) and OTC ...at **least** as low (in the sense of minmum squared error) as predictions using the ...

8

... paper, **Recursive** **Least** **Squares** Dictionary Learning Algorithm (RLS-DLA) is used to learn the initial dictionary for dictionary learning of sparse EIT ...

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... Abstractâ€” In this paper, a model free uncalibrated visual servoing algorithm based on **recursive** **least** **squares** is proposed and discussed in depth. No robot kinetics or dynamics, camera calibration or ...

9

... In this work, we present low-complexity variable forgetting factor (VFF) techniques for diffusion **recursive** **least** **squares** (DRLS) algorithms. Particularly, we propose low-complexity VFF-DRLS ...

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... decision-directed **recursive** **least** **squares** (DD-RLS) ...weighted **least** **squares** cost func- tion it is minimized and eventually the RLS MIMO channel estimation algorithm is ...

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... Acoustic echo cancellation (AEC) is a well-known application of adaptive filters in communication acoustics. To implement AEC for multichannel reproduction systems, powerful adaptation algorithms like the generalized ...

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... (1.15) Note that the least squares estimate of an arbitrary vector is thus obtained by premulti- plying by a matrix that is a function of only the input data vectors] The operator Pt(n) [r] ...

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... desired signal results in an additive error term and does not effect the weight vector. coefficients[r] ...

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... agreement in the two normalized weight error trajectories. Thus, the stabilization techniques had little impact on least squares performance for this example... Comparison of normalized [r] ...

142

... the **recursive** GLS inherits the recursion-oriented advantages of the conventional RLS ...the **recursive** GLS has much more reduced computational complexity than traditional LE-constrained GLS methods and is ...

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... This sequence is a zero mean white independent random process which has a variance related to signal statistics, the weight vector covariance, and the floating point errorso The calculat[r] ...

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... This section deals with the derivation of the statistical word length (SWL) required to quantize the Kalman gain vector elements in order to meet certain performance measures.. The metho[r] ...

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... a Addition of signed-binary and binary b Subtraction of binary from signed-binary Figure 2.4 Using full-adders to add a signed-binary and a binary number A single redundant input means t[r] ...

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... modified **recursive** **least** **squares** estimation algorithm RLS with dead zone cannot estimate parameters of ...modified **recursive** extended **least** **squares** estimation algorithm RELS with ...

8

... on **recursive** **least** **squares** (RLS) approximation and allows to determine the memoryless static nonlinearity as well as the linear model parameters from a linear set of ...The **recursive** algorithm ...

9

... the **least** mean **squares** (LMS) and **recursive** **least** **squares** (RLS) algorithms, are presented in their conventional linear and kernelised ...

5

... tional **recursive** **least** **squares** (RLS) using VFF based on the standard EPE criterion, and the adaptive M robust- based algorithm with only scale factor ...

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... Transversal Recursive Least Squares (RLS) algorithms estimate filter coefficients which minimize the accumulated sum of the square of the error residuals termed the error power.. In this[r] ...

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