Results 251 to 260 of about 102,342 (297)

Recursive Least Squares Estimation

2011
So far, we have considered the least squares solution to a particularly simple es- 3 timation problem in a single unknown parameter. A more general problem is the estimation of the n unknown parameters aj , j = 1, 2, . . . ,n, appearing in a general nth order linear regression relationship of the form, \( x(k)={a_1}{x_1}(k)+{a_2}{x_2}(k) +\cdots +{a_n}{
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Variable Regularized Square Root Recursive Least Square Method

IFAC Proceedings Volumes, 2012
In this paper, we develop the variable regularized square root recursive least square method which is in order to track time-varying parameters extended by an exponential forgetting factor (EF-VR-SRRLS). The proposed approach is arisen from the exact recursification of the original batch mode method solving the restricted quadratic problem.
Jakub Dokoupil, Vladimír Burlak
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Recursive least squares ladder estimation algorithms

IEEE Transactions on Circuits and Systems, 1981
Recursive least squares ladder estimation algorithms have attracted much attention recently because of their excellent convergence behavior and fast parameter tracking capability, compared to gradient based algorithms. We present some recently developed square root normalized exact least squares ladder form algorithms that have fewer storage ...
Lee, Daniel T. L., Morf, Martin, Friedlander, Benjamin   +2 more
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Fault-tolerant QRD recursive least squares

IEE Proceedings - Computers and Digital Techniques, 1996
The authors present an algorithm-based fault tolerant scheme for recursive least squares, appropriate for applications in adaptive signal processing. The technique is closely focused on the Gentleman-Kung-McWhirter triangular systolic array architecture for QR decomposition.
M.P. Connolly, P. Fitzpatrick
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Privacy Preserving Recursive Least Squares Solutions

2019 18th European Control Conference (ECC), 2019
Individual privacy is becoming a more prioritized issue in the modern world, because the world is becoming increasingly more digitized and citizens are starting to feel monitored. Private information could furthermore be misused in the wrong hands. Many control systems rely on data that often contain privacy sensitive information.
Katrine Tjell, Ignacio Cascudo, Rafael Wisniewski   +2 more
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Recursive least-squares sequence estimation

IBM Journal of Research and Development, 1994
A family of adaptive communication receivers based on recursive least-squares sequence estimation (RLSSE) algorithms is proposed which provides performance comparable to that of conventional linear receivers, but with reduced complexity and less sensitivity to channel mismatch.
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State-space recursive least squares: Part II

Signal Processing, 2004
This paper is a sequel of our earlier development of state-space recursive least squares (SSRLS). Stability and convergence analysis of SSRLS and its steady-state counterpart complete the theoretical framework of this new powerful algorithm. Upper bounds on the forgetting factor that ensure stability of the filter have been derived.
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Well-Conditioned Recursive Least-Squared Estimation Algorithms

IFAC Proceedings Volumes, 1992
This paper explains the inadequacies due to ill-conditioning of classical recursive least squares signal estimation algorithms based on Taylor series expansions, then shows how the algorithms may be restructured using orthogonal expansions, at little cost in extra complexity, to provide well-conditioned versions suitable for implementation in a variety
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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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