几个数值积分公式的积分型余项

本文给出几个常用的数值积分公式,如梯形公式、校正梯形公式和Simpson公式,以及对应的复合数值积分公式的积分型余项。     

利用积分证明Taylor公式

利用 Newton-Leibniz公式 ,给出了 Taylor公式的一种新的证明 .并由所得余项导出了其它形式的余项     

Simpson校正公式误差的精确表示

利用带有积分余项的Taylor公式重新推导了Simpson校正公式,同时给出了其误差的精确表示,而这一结果将优于Simpson校正公式[J]中的误差估计.     

中矩形公式与梯形公式的注记

将数值积分中的中矩形公式与梯形公式推广到两个函数的情形,并讨论了中间值的渐近性质.     

一类求积公式的构造方法

本文利用 Euler-Maclaurin求和公式构造了一类求积公式 ,称为修正复合梯形公式 .它和复合梯形公式的求积节点及计算量是一样的 ,但收敛阶有很大的提高 ,特别适合于计算带有各种类型小波的数值积分 .     

一类Gauss-Jacobi数值求积公式的极限性质

讨论了形如∫aa+h(x-a)βf(x)dx的Gauss-Jacobi求积公式,当积分区间长度趋向于零时,确定了求积公式的余项中介点η的渐近性,并给出了校正公式,比原公式提高了两次代数精度.此外,本文的结论包含了文[3]的结果.     

多重积分数值计算的梯形法余项及外推算法

研究了应用梯形法进行多重积分数值计算的余项的一般形式,为多重积分的外推算法提供了理论依据,同时提出了一种按积分变量逐维外推的数值计算方法.     

Cotes数值求积公式的校正

本文研究了Cotes数值求积公式代数精度的问题,给出了Cotes求积公式余项"中间点"的渐进性定理.利用该定理得到了改进的Cotes求积公式,并证明了改进后的Cotes求积公式比原来的公式具有较高的代数精度.     

一类求积公式的构造方法

本利用Euler-Maclaurin求和公式构造了一类求积公式,称为修正复合梯形公式。它和复合梯形公式的求积节点及计算量是一样的,但收敛阶有很大的提高,特别适合于计算带有种类型小波的数值积分。     

一类超收敛数值差商公式研究

本文针对充分光滑函数的数值差商公式的余项问题进行研究,对王兴华等于文[1]中提出的关于超收敛数值差商公式的猜想进行了证明,推广了该文中定理的适用范围,得到了比较广泛的一类超收敛的数值差商公式余项的lagrange表示．     

The Fourier-series method for inverting transforms of probability distributions

This paper reviews the Fourier-series method for calculating cumulative distribution functions (cdf's) and probability mass functions (pmf's) by numerically inverting characteristic functions, Laplace transforms and generating functions. Some variants of the Fourier-series method are remarkably easy to use, requiring programs of less than fifty lines. The Fourier-series method can be interpreted as numerically integrating a standard inversion integral by means of the trapezoidal rule. The same formula is obtained by using the Fourier series of an associated periodic function constructed by aliasing; this explains the name of the method. This Fourier analysis applies to the inversion problem because the Fourier coefficients are just values of the transform. The mathematical centerpiece of the Fourier-series method is the Poisson summation formula, which identifies the discretization error associated with the trapezoidal rule and thus helps bound it. The greatest difficulty is approximately calculating the infinite series obtained from the inversion integral. Within this framework, lattice cdf's can be calculated from generating functions by finite sums without truncation. For other cdf's, an appropriate truncation of the infinite series can be determined from the transform based on estimates or bounds. For Laplace transforms, the numerical integration can be made to produce a nearly alternating series, so that the convergence can be accelerated by techniques such as Euler summation. Alternatively, the cdf can be perturbed slightly by convolution smoothing or windowing to produce a truncation error bound independent of the original cdf. Although error bounds can be determined, an effective approach is to use two different methods without elaborate error analysis. For this purpose, we also describe two methods for inverting Laplace transforms based on the Post-Widder inversion formula. The overall procedure is illustrated by several queueing examples.     

A note on unconditionally stable linear multistep methods

It has been shown by Dahlquist [3] that the trapezoidal formula has the smallest truncation error among all linear multistep methods with a certain stability property. It is the purpose of this note to show that a slightly different stability requirement permits methods of higher accuracy.The preparation of this paper was sponsored by the Swedish Technical Research Council.     

The best quadrature formula based on Hermite information for the class KW~2[a,b]

The best quadrature formula has been found in the following sense:for afunction whose norm of the second derivative is bounded by a given constant and thebest quadrature formula for the approximate evaluation of integration of that function canminimize the worst possible error if the values of the function and its derivative at certainnodes are known.The best interpolation formula used to get the quadrature formula aboveis also found.Moreover,we compare the best quadrature formula with the open compoundcorrected trapezoidal formula by theoretical analysis and stochastic experiments.     

The best quadrature formula based on Hermite information for the class <Emphasis Type="Italic">KW</Emphasis><Superscript>2</Superscript> [<Emphasis Type="Italic">a,b</Emphasis>]

The best quadrature formula has been found in the following sense: for a function whose norm of the second derivative is bounded by a given constant and the best quadrature formula for the approximate evaluation of integration of that function can minimize the worst possible error if the values of the function and its derivative at certain nodes are known. The best interpolation formula used to get the quadrature formula above is also found. Moreover, we compare the best quadrature formula with the open compound corrected trapezoidal formula by theoretical analysis and stochastic experiments.     

The construction of high order convergent look-ahead finite difference formulas for Zhang Neural Networks

ABSTRACT

Zhang Neural Networks rely on convergent 1-step ahead finite difference formulas of which very few are known. Those which are known have been constructed in ad-hoc ways and suffer from low truncation error orders. This paper develops a constructive method to find convergent look-ahead finite difference schemes of higher truncation error orders. The method consists of seeding the free variables of a linear system comprised of Taylor expansion coefficients followed by a minimization algorithm for the maximal magnitude root of the formula's characteristic polynomial. This helps us find new convergent 1-step ahead finite difference formulas of any truncation error order. Once a polynomial has been found with roots inside the complex unit circle and no repeated roots on it, the associated look-ahead ZNN discretization formula is convergent and can be used for solving any discretized ZNN based model. Our method recreates and validates the few known convergent formulas, all of which have truncation error orders at most 4. It also creates new convergent 1-step ahead difference formulas with truncation error orders 5 through 8.     

The rate of convergence of a matrix power series

To estimate the truncation error of a matrix power series we need information about the magnitude of high powers of a matrix. Inequalities bearing on this question are surveyed, and their use is exemplified by calculations of bounds for the truncation error of the geometric series in matrices.     

The Mellin–Parseval formula and its interconnections with the exponential sampling theorem of optical physics

In this paper, we establish a Mellin version of the classical Parseval formula of Fourier analysis in the case of Mellin bandlimited functions, and its equivalence with the exponential sampling formula (ESF) of signal analysis, in which the samples are not equally spaced apart as in the classical Shannon theorem, but exponentially spaced. Two quite different examples are given illustrating the truncation error in the ESF. We employ Mellin transform methods for square-integrable functions.     

二元齐次矩阵Pad-型逼近及误差公式

二元矩阵Pade-型逼近的计算比较复杂.本文受Benouahmane和Cuyt的启发,通过引入一种变量代换,将二元齐次矩阵形式幂级数转化为一元含参数形式的矩阵形式幂级数,并给出了二元齐次矩阵Pade-型逼近的构造性的定义和误差公式的证明.数值实例说明了此方法的有效性.     

On smoothing and extrapolation for the trapezoidal rule

The trapezoidal formula applied to stiff systems of differential equations is stable, but produces slowly decaying oscillatory errors. A simple smoothing procedure which damps out the oscillations is given and it is shown that the discretization error after smoothing still contains only even powers of the stepsize. Some numerical examples reveal the power of the smoothing combined with extrapolation.     

A Direct Interpolation Method for Irregular Sampling

In this paper, band-limited functions are reconstructed from their values taken at a sequence of irregularly spaced sample points. We use a modified Lagrange formula, which is attributed to Boas and Bernstein. The formula used in this paper differs from the classical Boas–Bernstein formula in the following way. Instead of using infinite canonical products with respect to the whole sequence of sample points, we use canonical products with respect to sequences of sample points which are irregularly spaced only on finite intervals. Estimates for the truncation error of this reconstruction method are given.