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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.


Papers
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Journal ArticleDOI
01 Jul 1999
TL;DR: In this article, a neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance selftuning control (STC) strategy.
Abstract: A neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance self-tuning control (STC) strategy. In this way the controller can deal with nonlinear plants, which exhibit features such as uncertainties, nonminimum phase behaviour, coupling effects and may have unmodelled dynamics, and whose nonlinearities are assumed to be globally bounded. The unknown nonlinear plants to be controlled are approximated by an equivalent model composed of a simple linear submodel plus a nonlinear submodel. A generalised recursive least squares algorithm is used to identify the linear submodel and a layered neural network is used to detect the unknown nonlinear submodel in which the weights are updated based on the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model therefore the nonlinear submodel is naturally accommodated within the control law. Two simulation studies are provided to demonstrate the effectiveness of the control algorithm.

72 citations

Proceedings ArticleDOI
22 Oct 2007
TL;DR: It is revealed that processing motion-corrupted PPG signals by least mean squares (LMS) and recursive least squares (RLS) algorithms can be effective to reduce SpO2 and HR errors during jogging, but the degree of improvement depends on filter order.
Abstract: Wearable physiological monitoring using a pulse oximeter would enable field medics to monitor multiple injuries simultaneously, thereby prioritizing medical intervention when resources are limited. However, a primary factor limiting the accuracy of pulse oximetry is poor signal-to-noise ratio since photoplethysmographic (PPG) signals, from which arterial oxygen saturation (SpO2) and heart rate (HR) measurements are derived, are compromised by movement artifacts. This study was undertaken to quantify SpO2 and HR errors induced by certain motion artifacts utilizing accelerometry-based adaptive noise cancellation (ANC). Since the fingers are generally more vulnerable to motion artifacts, measurements were performed using a custom forehead-mounted wearable pulse oximeter developed for real-time remote physiological monitoring and triage applications. This study revealed that processing motion-corrupted PPG signals by least mean squares (LMS) and recursive least squares (RLS) algorithms can be effective to reduce SpO2 and HR errors during jogging, but the degree of improvement depends on filter order. Although both algorithms produced similar improvements, implementing the adaptive LMS algorithm is advantageous since it requires significantly less operations.

72 citations

Journal ArticleDOI
TL;DR: A weight least squares method which is based on the first order Taylor expansions of the noise terms is developed and it reduces the estimation bias arising from the least squares (LS) method when the target is outside the convex hull formed by sensors.

72 citations

Journal ArticleDOI
TL;DR: Dr. Bernard Widrow presents a personal view on the discovery of the least mean squares algorithm.
Abstract: Dr. Bernard Widrow presents a personal view on the discovery of the least mean squares algorithm.

72 citations

Journal ArticleDOI
TL;DR: In this paper, the Lagrange multiplier approach is used to find the limits of various model parameters consistent with a set of experimental data, and the physical interpretation of these limits and those implied by the parameter covariance matrix are discussed.
Abstract: An important problem in geophysical modeling involves the attempt to find the limits of various model parameters consistent with a set of experimental data. When the agreement between model and data can be described in terms of a quadratic form in the residuals, as is the case whenever linear least squares methods are applicable, then the range of parameter values consistent with the data is easily computed by using a Lagrange multiplier approach. This method results in limits which are different from those implied by the covariance matrix for the least squares solution. The differences are simply calculated but may often be substantial in magnitude. In this paper I derive an expression for the limits, discuss the physical interpretation of these limits and those implied by the parameter covariance matrix, and discuss the extension of linear techniques to quasi-linear techniques.

72 citations


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