Results 221 to 230 of about 30,959 (260)

Incomplete series expansion for function approximation

Structural and Multidisciplinary Optimization, 2007
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Albert A Groenwold, J E Rooda, L F P Etman   +2 more
exaly   +4 more sources

Justifiability of the ZDO approximation in terms of a power series expansion

Theoretica Chimica Acta, 1970
The Zero Differential Overlap Approximation cannot be justified for all-valence-electron calculations in terms of a power series expansion of the overlap matrix, because the expansion diverges.
N. A. B. Gray, A. J. Stone
exaly   +2 more sources

Inhomogeneous and simultaneous Diophantine approximation in Cantor series expansions

Journal of Mathematical Analysis and Applications
This paper is concerned with inhomogeneous and simultaneous Diophantine approximation for the nonautonomous dynamical system induced by Cantor series expansion. Let \(Q =\{q_k\}_{k\ge 1}\) be a sequence of positive integers with \(q_k\ge 2\) for all \(k\ge 1\). For any positive integer \(n\), the transformations \(T_{Q,n}:[0,1)\to[0,1)\) and \(T_{Q}^{n}
Baiyang Zhang
exaly   +3 more sources

Modeling of LED using Piecewise Linear Approximation and Maclaurin series Expansion

2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 2018
LED is a light emitting semiconductor device which is nowadays, swiftly replacing conventional lighting system, owing to its high intensity and low energy consumption. In order to understand the operating characteristics of LED, its modeling is required. The very basic modeling of LED can be done by considering it as a resistor.
Rachana Garg, Priya Mahajan
exaly   +2 more sources

UNIFORM DIOPHANTINE APPROXIMATION TO CANTOR SERIES EXPANSION

Fractals, 2021
In this paper, we study the uniform Diophantine approximation in the nonautonomous dynamic system generated by the Cantor series expansions, which is formulated by considering the following set: [Formula: see text] It is of Hausdorff dimension [Formula: see text] for [Formula: see text] and is countable for [Formula: see text] under the condition that
YAN HAN, CHAO MA
openaire   +1 more source

On the approximation by truncated sampling series expansions

Signal Processing, 1984
Abstract In this note we point out in which way the approximation of not necessarily bandlimited functions by sampling series is injured by additional truncation. We will also show how the rate of convergence is influenced by the features of the underlying kernel function.
S.E. Ries, V.W. Splettstößer
openaire   +1 more source

MMD-ARMA approximation to the Volterra series expansion

Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284), 2002
Nonlinear filtering based on the Volterra series expansion is a powerful universal tool in signal processing. Due to the problem of increased complexity for higher orders and filter lengths, approximations up to third order nonlinearities using linear FIR-filters and multipliers have been developed earlier called multimemory decomposition (MMD). In our
V.S. Kafka, U. Appel
openaire   +1 more source

Series expansions and approximations

2002
Chapter 12 dealt with infinite series of numbers and criteria for their convergence. In the same way one can study infinite series of functions $$\sum\limits_{k = 0}^\infty {{u_k}\left( x \right)} $$ where the functions uk (x) are all defined on a common interval.
Adi Ben-Israel, Robert Gilbert
openaire   +1 more source

Approximation ratio of the digits in Oppenheim series expansion

Publicationes Mathematicae Debrecen, 2008
Summary: This paper is concerned with the Hausdorff dimensions of some sets determined by the approximation ratio of the digits in Oppenheim series expansion. We give a general characterization on the Hausdorff dimensions of such sets. As its corollaries, we answer questions posed by J. Galambos.
Wang, Baowei, Wu, Jun
openaire   +1 more source

Fast Kernel Matrix Approximations By Series Expansions

2022
169 pages ; Kernel functions are used in a variety of scientific settings to measure relationships or interactions between elements of a vector space. For example, in machine learning, kernel functions are often used to describe similarity or covariance between data points in a feature space.
openaire   +2 more sources