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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
More filters
Proceedings ArticleDOI
01 Nov 1969
TL;DR: A new algorithm for identification from input-output measurements of a canonical form for discrete-time linear systems with disturbances having rational spectral densities is obtained by a formal application of the recursive least squares formula.
Abstract: A new algorithm for identification from input-output measurements of a canonical form for discrete-time linear systems with disturbances having rational spectral densities is obtained by a formal application of the recursive least squares formula. Although in this case the assumptions of the least squares method are violated, the algorithm is shown to converge in mean square using a stochastic approximation proof. The proposed algorithm is computationally more expensive than the corresponding stochastic approximation formula [1], but converges much faster and there are no problems with choice of the gain constant. The complexity of the algorithm still compares favourably with other methods [2], [3], owing to its on-line structure.

81 citations

Proceedings ArticleDOI
14 Apr 1983
TL;DR: Some aspects of dynamic convergence behavior are discussed, with conclusions supported by simulation of adaptive filter algorithm for constant envelope waveforms.
Abstract: An adaptive filter algorithm has been developed and introduced [1] for use with constant envelope waveforms, e.g., FM communication signals. It has proven capable of suppressing additive interferers as well as equalization, without the need for a priori statistical information. In this paper, aspects of dynamic convergence behavior are discussed, with conclusions supported by simulation.

80 citations

Journal ArticleDOI
TL;DR: Simulations for a system identification application show that the proposed scheme and algorithms outperform in convergence and tracking existing sparsity-aware algorithms.
Abstract: This letter proposes a novel sparsity-aware adaptive filtering scheme and algorithms based on an alternating optimization strategy with shrinkage The proposed scheme employs a two-stage structure that consists of an alternating optimization of a diagonally-structured matrix that speeds up the convergence and an adaptive filter with a shrinkage function that forces the coefficients with small magnitudes to zero We devise alternating optimization least-mean square (LMS) algorithms for the proposed scheme and analyze its mean-square error Simulations for a system identification application show that the proposed scheme and algorithms outperform in convergence and tracking existing sparsity-aware algorithms

80 citations

Patent
29 Mar 1976
TL;DR: In this article, an adaptive recursive filter is disclosed which comprises first and second adaptive transversal filters selectively coupled together to minimize the mean square error of the output data of recursive filter based upon observations of input data to the recursive filter.
Abstract: An adaptive recursive filter is disclosed which, in a preferred embodiment, comprises first and second adaptive transversal filters selectively coupled together to minimize the mean square error of the output data of the recursive filter based upon observations of input data to the recursive filter. Each transversal filter includes a tapped delay line with a variable weight on each tap. The output data of the recursive filter is developed by combining the outputs of the first and second transversal filters. The input data is applied to the first transversal filter, while the output data is applied to the second transversal filter. The output data is also combined with a reference signal to provide an error signal. A function of that error signal is utilized to update the weights of all of the taps in both transversal filters in order to cause the weights to automatically adapt themselves to minimize the mean square error of the output data of the recursive filter.

80 citations

Journal ArticleDOI
TL;DR: In this paper, an adaptive-predictive control algorithm is developed for a class of SISO nonlinear discrete-time systems based on a generalized predictive control (GPC) approach, which is model-free, based directly on pseudo-partial derivative derived on-line from the input and output information of the system using a recursive least squares type of identification algorithm.
Abstract: In this paper, an adaptive-predictive control algorithm is developed for a class of SISO nonlinear discrete-time systems based on a generalized predictive control (GPC) approach. The design is model-free, based directly on pseudo-partial-derivatives derived on-line from the input and output information of the system using a recursive least squares type of identification algorithm. The proposed control is especially useful for nonlinear systems with vaguely known dynamics. Robust stability of the closed-loop system is analyzed and proven in the paper. Simulation and real-time application examples are provided for real nonlinear systems which are known to be difficult to model and control.

80 citations


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