Exact initialization of the recursive least‐squares algorithm

Stoica, Petre; Åhgren, Per

doi:10.1002/acs.681

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…Other update strategies, akin to those considered in [36], can be explored in online applications where complexity needs to be further reduced. Compute ( 26) and (27) 10:…”

Section: B Online Formulationmentioning

confidence: 99%

Online Hyperparameter-Free Sparse Estimation Method

Zachariah

Stoica

2015

IEEE Trans. Signal Process.

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Abstract-In this paper we derive an online estimator for sparse parameter vectors which, unlike the LASSO approach, does not require the tuning of any hyperparameters. The algorithm is based on a covariance matching approach and is equivalent to a weighted version of the square-root LASSO. The computational complexity of the estimator is of the same order as that of the online versions of regularized least-squares (RLS) and LASSO. We provide a numerical comparison with feasible and infeasible implementations of the LASSO and RLS to illustrate the advantage of the proposed online hyperparameterfree estimator.

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“…Other update strategies, akin to those considered in [36], can be explored in online applications where complexity needs to be further reduced. Compute ( 26) and (27) 10:…”

Section: B Online Formulationmentioning

confidence: 99%

Online Hyperparameter-Free Sparse Estimation Method

Zachariah

Stoica

2015

IEEE Trans. Signal Process.

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“…and setting the derivative to zero yields the estimate (29). In the same way it can be seen that the minimum of d i,j is attained at (29).…”

Section: Appendix C Proof Of Theoremmentioning

confidence: 62%

“…and setting the derivative to zero yields the estimate (29). In the same way it can be seen that the minimum of d i,j is attained at (29). Insertingσ i andd i,j into (45), we see that we can find the minimizing Θ and Z by minimizing…”

Section: Appendix C Proof Of Theoremmentioning

confidence: 71%

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Recursive nonlinear-system identification using latent variables

2018

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In this paper we develop a method for learning nonlinear system models with multiple outputs and inputs. We begin by modelling the errors of a nominal predictor of the system using a latent variable framework. Then using the maximum likelihood principle we derive a criterion for learning the model. The resulting optimization problem is tackled using a majorization-minimization approach. Finally, we develop a convex majorization technique and show that it enables a recursive identification method. The method learns parsimonious predictive models and is tested on both synthetic and real nonlinear systems.

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“…, the next sequence, corresponding to recursive least squares method (Ljung, 1999;Stoica and Ahgren, 2002) can be used to provide an estimation of the uncertainty scalar term…”

Section: On-line Parameter Estimatormentioning

confidence: 99%

Discrete-Time Sliding-Mode WMR Control Based on Parameter Identification

Filipescu

Nunes

Stamatescu³

2005

IFAC Proceedings Volumes

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In this paper a discrete-time adaptive sliding mode controller for wheeled mobile robots (WMR) is presented. The dynamical model with time-varying mass has been taken into account. The sliding-mode controller has designed on two components, corresponding to angular and position motion, respectively. In order to accomplish the robustness against parameter uncertainties (robot mass and moment of inertia), an on-line closed loop identification scheme is proposed. In both, angular and position motion controllers, on-line estimation of parameters, which depend on robot mass, is used.

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Exact initialization of the recursive least‐squares algorithm

Abstract: We present an initialization procedure for the recursive least-squares (RLS) algorithm that has almost the same form as the RLS algorithm itself and which is exact in the sense that the so-initialized RLS estimate coincides with the batch LS estimate as soon as the latter exists.

Cited by 11 publications

References 3 publications

Online Hyperparameter-Free Sparse Estimation Method

Online Hyperparameter-Free Sparse Estimation Method

Recursive nonlinear-system identification using latent variables

Discrete-Time Sliding-Mode WMR Control Based on Parameter Identification

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