带绝对值符号的函数的积分法

本文拟通过一些例子探讨带绝对值符号的函数的定积分计算的规律和方法．一、基本方法解决这类积分的基本思路是：用分段函数表示被积函数，以便去掉绝对值符号，然后利用定积分的可加性，分段进行计算．1．找“零点”，分区间，脱去绝对值符号树三计算积分，其中E为闭区间[0，4π]中使积分式有意义的一切值所成之集合．解由已知条件知找“零点”，为此解方程cosx=0在积分区间上的“零点”为此时积分鞠间分成一般地，计算积分．我们就需要求出的所有“零点”，并用这些“零点”把积分区间分为几个部分区间，然后讨论f（X）在各部分区间上的…     

几类定积分不等式的证明

定积分不等式的证明，根据命题条件可大致分为1．已知被积函数仅具有连续性；2．已知被积函数一阶可导。且给出端点函数值或符号；3．已知被积函数二阶或二阶以上可导，且又知最高阶导数的符号，等三种类型尝试进行。     

符号函数的Legendre非线性逼近

1引言许多数学和物理工作者研究了逼近形式正交多项式级数的具有较好收敛性的非线性方法,如文献[2-5,9]．这些非线性逼近方法的一个共同点是使用了线性级数中正交多项式的母函数．众所周知,的符号函数具有很多的应用,如文献[7]利用符号函数的积分表示来分析相联存储器的回想过程．文献[1]及其中所引用的一些文献为了获得交迭格Dirac算子,讨论了符号函数的有理逼近和连分式展开．在本文中,我们研究符号函数的Lengendre     

对数三角函数的定积分

定义了三种积分表示的两元函数.这些两元函数有伽马函数表示,可以展开为幂级数.在积分符号内展开被积函数,先积分,再求和,也得到级数展开.对比展开系数,就得到一些对数三角函数定积分的值.选取合适的围道,得到其他两类对数三角函数定积分的值.     

二元函数全微分求积的一个简单方法

利用分离积分的方法，给出了二元函数全微分P（x，y）出＋Q（x，y）dy=du（x,y）求积的一个简便公式．其特点是只需计算函数P和函数Q的不定积分，从而避免原方法在积分路径选取时的麻烦以及由积分路径选取不当所导致的错误．     

二重积分的对称性问题

二重积分的对称性问题汪秀羌（华南理工大学，广州５１００４１）利用对称性计算二重积分，不但可以使计算简化，有时还可以避免错误．在一般情况下，必须是积分区域Ｄ具有对称性，而且被积函数对于区域Ｄ也具有对称性，才能利用对称性来计算．在特殊情况下，虽然积分区域...     

分割积分区间计算定积分

在定积分的计算中,当积分区间关于坐标原点对称且被积函数为奇函数或偶函数时很容易计算．当被积函数为非奇非偶函数时的计算方法是先分割积分区间再作变量替换,进一步给出任意区间上的定积分的计算有相同的计算方法．     

含有绝对值记号的函数积分的计算

在积分计算中,有时会遇到被积函数含有绝对值记号的情况.初学者对这类题常感困难.虽然知道应该先去掉绝对值记号再积分,但在各种情况下,怎样去掉绝对值记号却常常茫然.本文介绍两种方法,其一是用绝对值记号内函数的零点所构成的曲线、曲面将积分域分为若干子域,在每个子域上,函数的符号总是一致的,从而易于去掉绝对值记号.其二是利用对称性的方法.     

分段函数积分时应注意的几个问题

有些同学在计算积分题目,遇到被积函数显含或隐含分段函数的情况时,常常疏忽大意,带来不应有的错误.下面通过例子谈谈计算这类积分时注意的几个问题.     

无界函数的反常积分的计算

运用牛顿‐莱布尼茨公式和无界函数的反常积分的定义证明无界函数的反常积分的计算定理,运用这个定理计算无界函数的反常积分简单快捷,通过举例说明这个定理的应用,并指出多种大学数学参考书中存在的一个共同错误。     

Minimax-rate adaptive nonparametric regression with unknown correlations of errors In Celebration of Professor Lincheng Zhao's 75th Birthday

Minimax-rate adaptive nonparametric regression has been intensively studied under the assumption of independent or uncorrelated errors in the literature. In many applications, however, the errors are dependent,including both short-and long-range dependent situations. In such a case, adaptation with respect to the unknown dependence is important. We present a general result in this direction under Gaussian errors. It is assumed that the covariance matrix of the errors is known to be in a list of specifications possibly including independence, short-range dependence and long-range dependence as well. The regression function is known to be in a countable(or uncountablu but well-structured) collection of function classes. Adaptive estimators are constructed to attain the minimax rate of convergence automatically for each function class under each correlation specification in the corresponding lists.     

On estimation in multivariate linear calibration with elliptical errors

The estimation problem in multivariate linear calibration with elliptical errors is considered under a loss function which can be derived from the Kullback-Leibler distance. First, we discuss the problem under normal errors and give unbiased estimate of risk of an alternative estimator by means of the Stein and Stein-Haff identities for multivariate normal distribution. From the unbiased estimate of risk, it is shown that a shrinkage estimator improves on the classical estimator under the loss function. Furthermore, from the extended Stein and Stein-Haff identities for our elliptically contoured distribution, the above result under normal errors is extended to the estimation problem under elliptical errors. We show that the shrinkage estimator obtained under normal models is better than the classical estimator under elliptical errors with the above loss function and hence we establish the robustness of the above shrinkage estimator.     

Adaptive tuning of the fuzzy controller for robots

An adaptive tuning algorithm of the fuzzy controller is developed for a class of serial-link robot arms. The algorithm can on-line tune parameters of premise and consequence parts of fuzzy rules of the fuzzy basis function (FBF) controller. The main part of the fuzzy controller is a fuzzy basis function network to approximate unknown rigid serial-link robot dynamics. Under some mild assumptions, a stability analysis guarantees that both tracking errors and parameter estimate errors are bounded. Moreover, a robust technique is adopted to deal with uncertainties including approximation errors and external disturbances. Simulations of the proposed controller on the PUMA-560 robot arm demonstrate the effectiveness.     

On a conjecture of D. B. Hunter

We consider errors of positive quadrature formulas applied to Chebyshev polynomials. These errors play an important role in the error analysis for many function classes. Hunter conjectured that the supremum of all errors in Gaussian quadrature of Chebyshev polynomials equals the norm of the quadrature formula. We give examples, for which Hunter's conjecture does not hold. However, we prove that the conjecture is valid for all positive quadratures if the supremum is replaced by the limit superior. Considering a fixed positive quadrature formula and the sequence of all Chebyshev polynomials, we show that large errors are rare.     

Comparative Analysis of Cubic Spline and Kernel Estimation of a Probit Function

The least-squares cubic spline and the kernel estimators produce comparable mean squared errors, although the kernel produces smaller mean squared errors when the variable increases away from 0. Mean squared error increases with an increase in the number of knots (for the cubic spline) or reduced band width (for the kernel estimator). The cubic spline produces smaller mean squared errors when all observations are made at knots than when they are spaced out between knots. Irrespective of the exact form of the probit function g(x), the cubic spline estimator is asymptotically unbiased, while the kernel estimator only converges to g(x) under certain conditions. Moreover, the cubic spline is a smooth function, which is twice differentiable on the interval [0,1].     

Nonparametric variance function estimation with missing data

In this paper, a fixed design regression model where the errors follow a strictly stationary process is considered. In this model the conditional mean function and the conditional variance function are unknown curves. Correlated errors when observations are missing in the response variable are assumed. Four nonparametric estimators of the conditional variance function based on local polynomial fitting are proposed. Expressions of the asymptotic bias and variance of these estimators are obtained. A simulation study illustrates the behavior of the proposed estimators.     

The construction and approximation of feedforward neural network with hyp erb olic tangent function

In this paper, we discuss some analytic properties of hyperbolic tangent function and estimate some approximation errors of neural network operators with the hyperbolic tangent activation functionFirstly, an equation of partitions of unity for the hyperbolic tangent function is givenThen, two kinds of quasi-interpolation type neural network operators are constructed to approximate univariate and bivariate functions, respectivelyAlso, the errors of the approximation are estimated by means of the modulus of continuity of functionMoreover, for approximated functions with high order derivatives, the approximation errors of the constructed operators are estimated.     

Adaptive greedy approximations

The problem of optimally approximating a function with a linear expansion over a redundant dictionary of waveforms is NP-hard. The greedy matching pursuit algorithm and its orthogonalized variant produce suboptimal function expansions by iteratively choosing dictionary waveforms that best match the function’s structures. A matching pursuit provides a means of quickly computing compact, adaptive function approximations. Numerical experiments show that the approximation errors from matching pursuits initially decrease rapidly, but the asymptotic decay rate of the errors is slow. We explain this behavior by showing that matching pursuits are chaotic, ergodic maps. The statistical properties of the approximation errors of a pursuit can be obtained from the invariant measure of the pursuit. We characterize these measures using group symmetries of dictionaries and by constructing a stochastic differential equation model. We derive a notion of the coherence of a signal with respect to a dictionary from our characterization of the approximation errors of a pursuit. The dictionary elements slected during the initial iterations of a pursuit correspond to a function’s coherent structures. The tail of the expansion, on the other hand, corresponds to a noise which is characterized by the invariant measure of the pursuit map. When using a suitable dictionary, the expansion of a function into its coherent structures yields a compact approximation. We demonstrate a denoising algorithm based on coherent function expansions.     

删失数据回归误差项的非参数密度估计的一致相合性(英文)

回归误差项是不可观测的. 由于回归误差项的密度函数在实际中有许多应用, 故使用非参数方法对其进行估计就成为回归分析中的一个基本问题. 针对完全观测数据回归模型, 曾有作者对此问题进行了研究. 然而在实际应用中, 经常会有数据被删失的情况发生, 在此情况下, 可以利用删失回归残差, 并使用核估计的方法对回归误差项的密度函数进行估计. 本文研究了该估计的大样本性质, 并证明了估计量的一致相合性.     

相依误差广义线性模型的M估计的Bahadur表示

考虑如下广义线性模型y_i=h(x~T_i,β)+e_i=1,2,…,n,其中e_i=G(…,ε_(i-1),ε_i),h是一个连续可导函数,ε_i是独立同分布的随机变量,并且它的期望为0,方差σ~2有限.本文给出了参数β的M估计,并且得到了该估计的Bahadur表示,该结论推广了线性模型的相关结论.应用M估计的Bahadur表示,得到了相依误差的线性回归模型,poisson模型,logistic模型和独立误差的广义线性模型等模型的渐近性质.