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Parametric statistics

About: Parametric statistics is a research topic. Over the lifetime, 39200 publications have been published within this topic receiving 765761 citations.


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Journal ArticleDOI
TL;DR: In this paper, an example of exact parametric resonance in an extended system ruled by nonlinear partial differential equations of nonlinear Schrodinger type is studied, and the results have applicability in recent experiments in Bose-Einstein condensation and to classical problems in nonlinear optics.
Abstract: We study an example of exact parametric resonance in an extended system ruled by nonlinear partial differential equations of nonlinear Schr\"odinger type. It is also conjectured how related models not exactly solvable should behave in the same way. The results have applicability in recent experiments in Bose-Einstein condensation and to classical problems in nonlinear optics.

135 citations

Journal ArticleDOI
TL;DR: Computational results indicate that the parametric approach is orders of magnitude faster than the K -th shortest path approach for most problems tested, and for problems with a positive correlation between the two cost coefficients, the parametrical approach is seen to be significantly fasterthan the label setting approach.

135 citations

Journal ArticleDOI
TL;DR: In this paper, the distribution of the displacements within the foundation has been estimated using variational calculus, and the value of γ is shown to be a function of some nondimensional parameters of the beam, the foundation and the mode of loading.
Abstract: Even though the two‐parameter model developed by Vlasov for beams on elastic foundations represents the interaction between the beams and the foundation better than the Winkler model, it requires an estimation of a third parameter, γ, which represents the distribution of the displacements within the foundation. Using variational calculus, the value of γ is shown to be a function of some nondimensional parameters of the beam, the foundation, and the mode of loading. By using an iterative procedure, a consistent value of γ is calculated. An example of a beam carrying a uniformly distributed load is solved and the results are compared with a more rigorous finite element solution.

135 citations

Journal ArticleDOI
TL;DR: This paper considers designing adaptive finite-time controllers for a class of SISO strict feedback nonlinear plants with parametric uncertainties based on given specifications with requirement on transient response in terms of convergence time and convergence rate.

135 citations

Journal ArticleDOI
TL;DR: A new iterative learning control (ILC) scheme for nonlinear systems with parametric uncertainties that are temporally and iteratively varying and designed to effectively handle the unknown basis functions is proposed.
Abstract: In this technical note, we propose a new iterative learning control (ILC) scheme for nonlinear systems with parametric uncertainties that are temporally and iteratively varying. The time-varying characteristics of the parameters are described by a set of unknown basis functions that can be any continuous functions. The iteratively varying characteristics of the parameters are described by a high-order internal model (HOIM) that is essentially an auto-regression model in the iteration domain. The new parametric learning law with HOIM is designed to effectively handle the unknown basis functions. The method of composite energy function is used to derive convergence properties of the HOIM-based ILC, namely the pointwise convergence along the time axis and asymptotic convergence along the iteration axis. Comparing with existing ILC schemes, the HOIM-based ILC can deal with nonlinear systems with more generic parametric uncertainties that may not be repeatable along the iteration axis. The validity of the HOIM-based ILC under identical initialization condition (i.i.c.) and the alignment condition is also explored.

135 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20252
20242
20233,966
20227,822
20211,968
20202,033