Topic
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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TL;DR: In this article, the Gauss method of least squares has been extended to the viewpoint of linear filter theory, and it is shown that all results obtained via the linear filter development are special cases of results obtained from the method of the least squares viewpoint.
Abstract: A tutorial guide is presented to the subject of state estimation. The development is presented as an elaboration of the Gauss method of least squares, and consequently differs in viewpoint somewhat from the more conventional development, which is from the viewpoint of linear filter theory. It is shown that all results obtained via the linear filter development are special cases of results obtainable from the method of least squares viewpoint.
51 citations
TL;DR: In this paper, first and second order least squares methods are used in generating simple approximation polynomials for the state term of the model for regenerative chatter in the milling process.
Abstract: First and second order least squares methods are used in generating simple approximation polynomials for the state term of the model for regenerative chatter in the milling process. The least squares approximation of delayed state term and periodic term of the model does not go beyond first order. The resulting discrete maps are demonstrated to have same convergence rate as the discrete maps in other works that are based on the interpolation theory. The presented discrete maps are illustrated to be beneficial in terms of computational time (CT) savings that derive from reduction in the number of calculation needed for generation system monodromy matrix. This benefit is so much that computational time of second order least squares-based discrete map is noticeably shorter than that of first order interpolation-based discrete map. It is expected from analysis then verified numerically that savings in CT due to use of least squares theory relative to use of interpolation theory of same order rises with rise in order of approximation. The experimentally determined model parameters used for numerical calculations are extracted from literature.
51 citations
20 Mar 2016
TL;DR: Numerical evaluations show that the proposed OLSTEC algorithm gives faster convergence per iteration comparing with the state-of-the-art online algorithms.
Abstract: We propose an online tensor subspace tracking algorithm based on the CP decomposition exploiting the recursive least squares (RLS), dubbed OnLine Low-rank Subspace tracking by TEnsor CP Decomposition (OLSTEC). Numerical evaluations show that the proposed OLSTEC algorithm gives faster convergence per iteration comparing with the state-of-the-art online algorithms.
51 citations
TL;DR: In this paper, a new identification technique is developed providing recursive parameters estimation of fractional order models, defined by a generalized ARX structure obtained by discretization of a continuous fractional-order differential equation.
51 citations
TL;DR: The Gauss-Newton method for solving nonlinear least squares problems is studied under the hypothesis that the derivative of the function associated with the least square problem satisfies a majorant condition, and a local convergence analysis is presented.
51 citations