Results 301 to 310 of about 670,956 (337)

Nonlinear Estimation and Asymptotic Approximations

Econometrica, 1978
central objective of this paper is to present a series expansion of nonlinear estimators in order to facilitate an analysis of the distributions of such estimators. Where the estimator under consideration is a maximum likelihood estimator, the method provides somewhat more information, as well as higher order approximations to the distributions of the ...
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Approximation by nonlinear wavelet networks

[Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, 1991
By combining the class of feedforward neural networks and results from the wavelet theory, a class of networks call wavelet networks that can be used to approximate any nonlinear function is proposed. A stochastic gradient procedure for black-box identification of nonlinear static systems based on this class of networks is developed.
Qinghua Zhang, Albert Benveniste
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Nonlinear Nonnested Spline Approximation

Constructive Approximation, 2016
Linear and in particular non-linear spline approximation is a most useful tool in the approximation of for instance two-dimensional functions. Usually, piecewise polynomial splines with more and more refined knot-sequences are considered as elements of nested spaces spanned by splines. Generalising from this point of view, it is interesting to consider
Lind, Martin, Petrushev, Pencho
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Nonlinear approximation

Acta Numerica, 1998
This is a survey of nonlinear approximation, especially that part of the subject which is important in numerical computation. Nonlinear approximation means that the approximants do not come from linear spaces but rather from nonlinear manifolds. The central question to be studied is what, if any, are the advantages of nonlinear approximation over the ...
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Nonlinear Approximation and Muckenhoupt Weights

Constructive Approximation, 2006
In the general atomic setting of an unconditional basis in a (quasi-) Banach space, we show that representing the spaces of m-terms approximation as Lorentz spaces is equivalent to the verification of two inequalities (Jackson and Bernstein), and that the validity of these two properties is equivalent to the Temlyakov property. The proof is very direct
Kerkyacharian, G., Picard, D.
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On the convex approximation of nonlinear inequalities

Optimization, 1974
The present paper deals with sufficient conditions for a system of convex incqualities to be a local approximation of a given arbitrary system in the following sense: the solution set of the first system is tangential to the solution set of the second one at the point under consideration. a criterion is proposed. For the case, in which the given system
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Nonlinear smoothing: approximate algorithms

Applied Mathematics and Computation, 1979
In this paper the method of stochastic linearization is employed to develop new approximate algorithms for nonlinear smoothing. Both fixed-point and fixed-interval smoothing cases are considered. An example is included to illustrate the use of the algorithms.
Chan, W. K., Kumar, K. S. P.
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Discrete Nonlinear Mean Approximation

ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, 1977
AbstractBest approximation on a finite set by a non‐linear family of functions with respect to a general sum “norm”, which includes as a special case the Lp norms (1 < p < ∞), is considered. Properties of best approximations are given. It is shown that a local minimum of the error may not be a global minimum. Computation of best approximations is
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Nonlinear Approximation with Local Fourier Bases

Constructive Approximation, 2000
It is shown that local Fourier bases are unconditional bases for the modulation spaces on \(R\), including the Bessel potential spaces and the Segal algebra \(S_0\). As a consequence, the abstract function spaces that are defined by the approximation properties with respect to a local Fourier basis, are precisely the modulation spaces.
Gröchenig, Karlheinz, Samarah, Salti
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Nonlinear system approximations

26th IEEE Conference on Decision and Control, 1987
We are interested in approximating a nonlinear system by a feedback linearizable system instead of a linear system. Two approaches presently exist in the literature. One involves the concept of involutivity to a certain order, and the other considers a canonical expansion and pure feedback approximation.
Richard Goldthwait, L. Hunt
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