• Journal of applied mathematics & informatics
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• v.17 no.1_2_3
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• pp.701-707
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• 2005

Delta sequences play an important role in convergence and approximation theory. Much of classical approximation theory is based on delta sequence. The rate of convergence of certain types of these sequences in Sobolev spaces has recently been studied. Here we estimate convergence rate of convolution type delta sequence in higher dimension.

• Journal of applied mathematics & informatics
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• v.14 no.1_2
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• pp.267-275
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• 2004

While the convergence of the classical Fourier series has been well known, the rate of its convergence is not well acknowledged. The results regarding the rate of convergence of the Fourier series and wavelet expansions can be found in the book of Walter[5]. In this paper, we give the rate of convergence of hybrid sampling series associated with orthogonal wavelets.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.43-46
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• 1999

Blind equalizers recover the transmitted data using signal's statistical characteristics only. Because of its computational simplicity and fast convergence rate, CMA is widely used in practice. Blind equalizers, however, converge much slowly than conventional equalizers which use the training signals. In order to improve the convergence rate, many modified blind equalization algorithms have been proposed. Among those, Normalized CMA (NCMA) was applied to increase the convergence rate by using the large step size. Unfortunately it can only be applied for the constant modulus signal constellation scheme. this paper, we propose the Selective NCMA (SNCMA) that improve the convergence rate of blind equalization algorithms by using NCMA for non-constant modulus signalling method such as QAM constellation. We achieved fast start-up convergence rate and reduced steady-state residual error.

• Bulletin of the Korean Mathematical Society
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• v.44 no.1
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• pp.125-130
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• 2007

We propose a simple and intuitive method to derive the exact convergence rate of global $L_{2}-norm$ error for strong numerical approximation of stochastic differential equations the result of which has been reported by Hofmann and $M{\"u}ller-Gronbach\;(2004)$. We conclude that any strong numerical scheme of order ${\gamma}\;>\;1/2$ has the same optimal convergence rate for this error. The method clearly reveals the structure of global $L_{2}-norm$ error and is similarly applicable for evaluating the convergence rate of global uniform approximations.

• Bulletin of the Korean Mathematical Society
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• v.53 no.5
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• pp.1549-1566
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• 2016

Denote $M_n$ the maximum of n independent and identically distributed variables from the generalized short-tailed symmetric distribution. This paper shows the pointwise convergence rate of the distribution of $M_n$ to exp($\exp(-e^{-x})$) and the supremum-metric-based convergence rate as well.

• Journal of the Chungcheong Mathematical Society
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• v.19 no.2
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• pp.133-139
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• 2006

By combining the classical Newton's method with the pseudo-secant method, pseudo-secant-Newton's method is constructed and its order and rate of convergence are investigated. Given a function $f:\mathbb{R}{\rightarrow}\mathbb{R}$ that has a simple real zero ${\alpha}$ and is sufficiently smooth in a small neighborhood of ${\alpha}$, the convergence behavior is analyzed near ${\alpha}$ for pseudo-secant-Newton's method. The order of convergence is shown to be cubic and the rate of convergence is proven to be $\(\frac{f^{{\prime}{\prime}}(\alpha)}{2f^{\prime}(\alpha)}\)^2$. Numerical experiments show the validity of the theory presented here and are confirmed via high-precision programming in Mathematica.

• Honam Mathematical Journal
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• v.29 no.3
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• pp.367-375
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• 2007

For bounded negatively associated random variables we derive almost sure convergence and specify the associated rate of convergence by establishing exponential inequality.

• Kyungpook Mathematical Journal
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• v.45 no.4
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• pp.571-577
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• 2005

The aim of this paper is to establish the rate of convergence of Baskakov-Durrmeyer operators for bounded variation function. We have given the better estimate over the results due to Guo ([4]), Anial and Teberska ([1]) and Gupta and Srivastava ([8]).

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.2
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• pp.108-114
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• 1996

We present proofs of the stability and convergence of Self-organizing feature map (SOFM) neural network with time-invarient learning rate and binary reinforcement function. One of the major problems in Self-organizing feature map neural network concerns with learning rate-"Kalman Filter" gain in stochsatic control field which is monotone decreasing function and converges to 0 for satisfying minimum variance property. In this paper, we show that the stability and convergence of Self-organizing feature map neural network with time-invariant learning rate. The analysis of the proposed algorithm shows that the stability and convergence is guranteed with exponentially stable and weak convergence properties as well.s as well.

• Journal of Integrative Natural Science
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• v.6 no.1
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• pp.53-56
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• 2013

We are interested in the rate of convergence of solutions of 2D Navier-Stokes equations in a smooth bounded domain as the viscosity tends to zero under Navier friction condition. If the initial velocity is smooth enough($u{\in}W^{2,p}$, p>2), it is known that the rate of convergence is linearly propotional to the viscosity. Here, we consider the rate of convergence for nonsmooth velocity fields when the gradient of the corresponding solution of the Euler equations belongs to certain Orlicz spaces. As a corollary, if the initial vorticity is bounded and small enough, we obtain a sublinear rate of convergence.