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Recursive Least-Squares Transversal Algorithms

1990
In Chap. 2, we discussed the recursive laws of the Normal Equations, and in Chap. 4, we saw how these properties can be used to obtain fast processing schemes for solving the Normal Equations in the recursive case based on the Givens reduction. This chapter is devoted to the recursive least-squares (RLS) algorithms based on a transversal predictor ...
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Regularized fast recursive least squares algorithms

International Conference on Acoustics, Speech, and Signal Processing, 2002
Chandrasekhar type factorization is used to develop new fast recursive least squares (FRLS) algorithms for finite memory filtering. Statistical priors are used to get a regularized solution which presents better numerical stability properties than that of the conventional least squares one.
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Recursive Least Squares Regression Analysis

1984
The recursive least squares (RLS) algorithm II developed in the previous Chapter provides a general method of estimating the parameters in a multi-parameter regression model. But, as we have pointed out, the algorithm is a deterministic estimation procedure in the sense that it makes few assumptions about either the statistical nature of the signals or
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Sparsity regularized recursive total least-squares

Digital Signal Processing, 2015
This paper introduces a new family of recursive total least-squares (RTLS) algorithms for identification of sparse systems with noisy input vector. We regularize the RTLS cost function by adding a sparsifying term and utilize subgradient analysis. We present ? 1 norm and approximate ?
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Recursive Least Squares

Teaching Statistics, 1987
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Data-Reuse Recursive Least-Squares Algorithms

IEEE Signal Processing Letters, 2022
Constantin Paleologu   +2 more
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Fault Tolerant Recursive Least Squares Minimization

1991
Existing fault tolerance schemes have often been ignored by systolic array designers because they are too costly and unwieldy to implement. With this in mind, we have developed a new technique specially tailored for recursive least squares minimization that emphasizes simplicity.
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Why the seahorse tail is square

Science, 2015
Michael M Porter   +2 more
exaly  

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