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This paper investigates the absolute convergence of the Fourier Laplace series concerning of some smooth functions defined on the unit sphere in R n,hereinto shows that:if f is 2([n4]+1) th continuously differentiable function on H r P,then the series ∑∞k=0Y kf converges uniformly to f.

讨论了n 维球面上某些可微函数类的Fourier Laplace级数的绝对收敛性,其中指出:设f是Hrp上 2 ( [n4 ]+1)次连续可微函数,则级数∑∞k =0 Ykf一致收敛到f参

One bases on function value and another bases on arithmetic average difference quotient. In this paper, the author mainly studies the approximation properties of both the function value and the derivative of two kinds of splines interpolation mentioned before, and the optimal error constants are obtained finally.

文中分别就这两类插值样条的函数值及一阶导数的逼近性质展开讨论,相应得出这两种插值函数在被插函数分别为一阶连续可导和二阶连续可导情况下的最佳误差常数。

With the help of the fundamental theory to singular measure differential system, Green's function matrix expression of solution and well-posed boundary condition are given, together with discussing the properties of the Green's function matrix under the influence of impulsive effect In section 5, based on Lyapunov function method, we investigate that the existence of the first boundary value problem to second-order singular system by macthing the solutions to the second boundary value problem; and the existence and uniqueness of three-point boundary value problem to third order singular system by macthing the solutions to two-piont boundary value problem.

第五部分借助Lyapunov函数法,通过将第二类边值问题的解对结成第一类边值问题的解,得到了二阶广义系统的第一类边值问题解的存在性;通过将两点边值问题的解对结成三点边值问题的解,得到了三阶广义系统的三点边值问题解的存在性和唯一性。

In the paper, a modified Similarity Measure Function, called Dsim, is proposed in order to solve the problem of non-contrasting behavior high dimensional space, and then a learning algorithm, called DCFCM, is advanced through the combination of Dsim and Approximated K-median and applied in intrusion detection which solves the problem of sharp border effecting problems and realizes the detection of abnormal incurrence.

本文将首先提出新的相似度函数Dsim,有效地解决了高维空间聚类选维和降维问题,实现了高效的聚类了;接着将Dsim与近似K-medians算法相结合,提出了新的模糊聚类算法----DCFCM,并将其用于入侵检测。解决了由尖锐边界、孤立点所带来的误报警和漏报警问题,实现了对异常行为的检测。

Using the classes from the previous two exercises, add a pair of static member functions to class Bar. The first static, named FooVal, should return the value of class Bar's static member of type Foo. The second member, named callsFooVal, should keep a count of how many times xval is called.

使用上面两题中定义的类,给 Bar 类增加一对成员函数:第一个成品命名为 FooVal,返回 Bar 类的 Foo 类型 static 成员的值;第二个成员命名为 callsFooVal,保存 xval 被调用的次数。

In this paper, based on the analysis of the core concepts in the Kernel method, the basic principles of the multiclass classification based on the one-class classification were studied, and the reliability function applied to the multiclass classification was put forward, which makes the classif.

在分析一种非线性数据处理新方法的核心概念基础上,研究了基于一类分类方法的多类分类基本原理,提出了应用于多类分类的可信度函数,使聚类与分类的结果更具有可信度。

We define I-quasi-invex vector function ., I-strictly quasi-invex vector function and KT-I-strictly quasi invex vector function, and derive the above equivalent condition for unconstrained or constrained multiobjective programming.

于是,在本文的第三部分,我们定义了Ⅰ类不变拟凸、Ⅰ类严格不变拟凸、KT-Ⅰ类严格不变拟凸的向量值函数,并且在无约束或约束多目标规划中,获得了每个驻点是有效解的等价条件。

In order to study these algorithms systematically and deeply, they are reviewed in this paper based on c-means algorithm, from metrics, entropy, and constraints on membership function or cluster centers. Moreover, the advantages and disadvantages of the typical fuzzy partitional algorithms are discussed.

为了能更为系统和深入地了解这些聚类算法及其性质,本文从改变度量方式、改变约束条件、在目标函数中引入熵以及考虑对聚类中心进行约束等几个方面,对在C-均值算法的基础上得到的基于划分的模糊聚类算法作了综述和评价,对各典型算法的优缺点进行了实验比较分析。

The best theoretical solution to a partitional clustering problem is to find all possible partitions and choose the partition that minimizes the objective function. However, the enumeration of all possible partitions is impractical even for data sets of moderate size. Therefore, as an alternative, most partitional approaches use an iterative optimization technique. And this could be cause local minimum.

本文在分析了目前聚类的基本方法的基础上,指出以K平均法等为代表的分割聚类虽然理论上找到类的所有划分并使得目标函数最小就可以获得最优解,但由于对于相当尺度的数据集列举所有可能的划分并不现实,因而多数分割方法采用迭代优化技术作为变通。

There is upcasting mechanism, it just extracts dictionary of a base class from a dictionary tuple, so you can run a function that requires base class from a function that requires subclass

有一个向上转型机制,从字典对中取出基类的字典,于是对子类参数调用基类参数的函数成为可能。

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