逼近

A new algorithm is presented in this paper for piecewise quadric B-splines curve reconstruction from scattered data in a plane.

基于控制顶点扰动的思想提出了一种新的曲线重构算法，用于构造一条分段二次B样条曲线来逼近平面上的散乱数据点。

The Radical asis Function Neural Network is a feed-forward NN model with the capacity of local approximation, so it can be used as a multiple-user detector in the CDMA.

径向基函数神经网络是一种具有局部逼近能力的前向神经网络模型，它可用作码分多址中的多用户检测器，并具有较好的检测性能；但是存在不足，主要表现在网络的构造和训练算法的优化方面。

Radical basis function neural networks have the powerful approximating capability, simple structure and outstandinglearning speed in comparison with other sorts of neural networks.

径向基函数神经网络具有其它前向神经网络所不具有的最佳逼近功能，并且结构简单，训练速度快。

If the method is combined with the subdivision method of curve,the rational spline curve with higher-order continuity is presented which is the approximation of offset curve.

该方法与离散算法相结合，可得到等距曲线的高阶连续的有理样条逼近曲线，最后，通过数值实例与已有方法作了比较。

With regard to the accuracy and smoothness of a set of discrete data points approximation, this paper gave a whole fairing and approximation algorithm of cubic Non-Uniform Rational B-Spline curve.

针对离散数据点序列的拟和精度及光顺度问题，提出了一种三次非均匀有理B样条曲线整体光顺逼近算法。

Padé approximation is an effective rational approximation.Using the first derivative of Gaussian function as basic wavelet,Pad conversion is used to obtain its rational fraction approximation.

采用高斯函数一阶导数作为基本小波，用Padé变换获得其有理分式逼近，利用开关电流技术实现小波滤波器可获得小波变换所需的基本小波及其膨胀小波。

Efficient and accurate methods for the static and dynamic reanalysis for large structural modifications were proposed.

中文摘要：本课题研究帕德逼近方法在求解若干线性、非线性力学问题中的应用。

In this paper, we study the iteration method approximating zero points of m-accretive operators in reflexive Banach space. The strong convergence of the revised method is proved. These results extend and improve corresponding results of others.

文章在自反Banach空间中研究了m-增生算子的零点迭代逼近问题，证明了修正的迭代算法强收敛到m-增生算子A的一个零点，此结果推广和改进了一些相关结论。

In the view of statistical learning, to establish EOR predictive model substantially is to solve a function regression problem.

建立提高采收率潜力预测模型从统计学习的角度来看，实质是属于函数逼近问题。

The used SVM kernel function can not approach to any object function in regression.

针对目前使用的SVM核函数在回归中不能逼近任意目标函数的问题，在支持向量机的核方法和小波框架理论的基础上，提出了LS-WSVM结构模型。

The split between the two groups can hardly be papered over.

这两个团体间的分歧难以掩饰。

This approach not only encourages a greater number of responses, but minimizes the likelihood of stale groupthink.

这种做法不仅鼓励了更多的反应，而且减少跟风的可能性。