Topic
Linear approximation
About: Linear approximation is a research topic. Over the lifetime, 3901 publications have been published within this topic receiving 74764 citations.
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TL;DR: In this article, a simple linear programming approach is proposed to approximate the integer nonlinear programming problem with multiple separable linear constraints, where each subsystem has multiple component choices and the problems generalize the typical series-parallel reliability problems when the number of component choices for each subsystem is set to one.
89 citations
TL;DR: In this paper, the impact of nonlinear distortions on the linear system identification framework is studied and a fast approximate nonlinear modelling framework is set up that is a natural extension of the linear framework, and bridges the gap between the linear and the nonlinear identification approaches.
88 citations
06 Jan 2002
TL;DR: A new algorithm that also has a correctness guarantee but whose worst-case running time is O(n log n) where n is the input size and this is actually optimal.
Abstract: A surface reconstruction algorithm takes as input a set of sample points from an unknown closed and smooth surface in 3-d space, and produces a piece-wise linear approximation of the surface that contains the sample points. Recently, several algorithms with a correctness guarantee have been proposed. They have unfortunately a worst-case running time that is quadratic in the size of the input because they are based on the construction of 3-d Voronoi diagrams or Delaunay tetrahedrizations which can have quadratic size. In this paper, we describe a new algorithm that also has a correctness guarantee but whose worst-case running time is O(n log n) where n is the input size. This is actually optimal. As in some of the previous algorithms, the piece-wise linear approximation produced by the new algorithm is a triangulation which is a subset of the 3-d Delaunay tetrahedrization.
87 citations
TL;DR: In this article, a multi-geometry and multi-physics model is developed for a Li-ion battery module which includes three cells connected in series by electrical busbars, and the model can be used to predict the 3D profiles of the electrical potentials and temperature in the battery.
87 citations
TL;DR: A generalized algorithm for on-line identification of a stochastic linear discrete-time system using noisy input and output measurements is presented and shown to converge in the mean-square sense.
Abstract: The parameter identification problem in the theory of adaptive control systems is considered from the point of view of stochastic approximation. A generalized algorithm for on-line identification of a stochastic linear discrete-time system using noisy input and output measurements is presented and shown to converge in the mean-square sense. The algorithm requires knowledge of the noise variances involved. It is shown that this requirement is a disadvantage associated with on-line identification schemes based on minimum mean-square-error criteria. The paper also presents two off-line identification schemes which utilize measurements obtained from repeated runs of the system's transient response and do not require explicit knowledge of the noise variances. These algorithms converge with probability one to the true parameter values.
86 citations