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Recursive least squares filter

About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.


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Journal Article
TL;DR: An optimal two-stage identification algorithm is presented for Hammerstein–Wiener systems where two static nonlinear elements surround a linear block and is shown to be convergent in the absence of noise and convergence with probability one in the presence of white noise.
Abstract: An optimal two-stage identification algorithm is presented for Hammerstein–Wiener systems where two static nonlinear elements surround a linear block. The proposed algorithm consists of two steps: The first one is the recursive least squares and the second one is the singular value decomposition of two matrices whose dimensions are fixed and do not increase as the number of the data point increases. Moreover, the algorithm is shown to be convergent in the absence of noise and convergent with probability one in the presence of white noise.

398 citations

Journal ArticleDOI
01 Mar 2012-Energy
TL;DR: In this article, the authors present a method to estimate the state-of-charge (SOC) of a lithium-ion battery, based on an online identification of its open-circuit voltage (OCV), according to the battery's intrinsic relationship between the SOC and the OCV for application in electric vehicles.

396 citations

Journal ArticleDOI
TL;DR: In this paper, perturbation theory for the pseudo-inverse (Moore-Penrose generalized inverse), for the orthogonal projection onto the column space of a matrix, and for the linear least squares is surveyed.
Abstract: This paper surveys perturbation theory for the pseudo–inverse (Moore–Penrose generalized inverse), for the orthogonal projection onto the column space of a matrix, and for the linear least squares ...

393 citations

01 Jan 2007
TL;DR: In this paper, the message-passing approach to model-based signal processing is developed with a focus on Gaussian message passing in linear state-space models, which includes recursive least squares, linear minimum-mean-squared-error estimation, and Kalman filtering algorithms.
Abstract: The message-passing approach to model-based signal processing is developed with a focus on Gaussian message passing in linear state-space models, which includes recursive least squares, linear minimum-mean-squared-error estimation, and Kalman filtering algorithms. Tabulated mes- sage computation rules for the building blocks of linear models allow us to compose a variety of such algorithms without additional derivations or computations. Beyond the Gaussian case, it is emphasized that the message-passing approach encourages us to mix and match different algorithmic tech- niques, which is exemplified by two different approachesV steepest descent and expectation maximizationVto message passing through a multiplier node.

389 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022104
2021172
2020228
2019234
2018237