If we want to be a bit more precise, we know that when we change by t, t that's for linear approximation to how the function changes.
如果我们想更加精确一点，我们知道当t变化的时候，乘以导数就出现了，well, t, times, the, derivative, comes, in，这是函数变化量的线性近似。
As one of the most important capability of ANN, function approximation ability can be used to design ANN model, which can characterize certain physics object.
函数逼近能力是ANN具有的重要性能之一，依据ANN具有的函数逼近能力，可用ANN模型去替代一个确定的物理对象。
The numerical example shows that increasing the item of radial basis function is not a right way to improve the accuracy of results, and more approximation functions should be employed in the DRBEM.
计算结果表明增加径向基函数的项数并不是改善结果的好办法，应该将更多的函数引入双互易边界元法中。
This method is also suited to design approximation function of passive filter.
这种方法同样适用无滤波器的近似函数设计。
The coupled cluster method is improved with the random phase approximation (RPA) to calculate vacuum wave function and vacuum energy of 2 + 1-d SU (2) lattice gauge theory.
采用无规相近似(RPA)耦合集团展开方法，计算出2 + 1维su(2)格点规范场的三到六阶真空波函数和真空能量。
This theorem simplifies greatly the analysis of the function approximation ability of FFMLNN because one needs only to study the one dimensional function approximation ability of FFMLNN.
也就是说我们只需研究其一维函数逼近能力，所得的结论完全适合于多维情形，该定理大大简化了前馈多层神经网络函数逼近问题的分析难度。
Due to its structural simplicity, the radial basis function (RBF) neural network has been widely used for approximation and classification.
径向基函数(RBF)神经网络因其结构简单而被广泛地用于非线性函数近似和数据分类。
The aim of Multiscale Geometric Analysis is to find a kind of optimal representation of high dimension function in the sense of nonlinear approximation.
多尺度几何分析旨在构建最优逼近意义下的高维函数表示方法。
With the best polynomial approximation as a metric, the rate of approximation of the neural networks with single hidden layer to a continuous function is estimated by using a constructive approach.
以最佳多项式逼近为度量，用构造性方法估计单隐层神经网络逼近连续函数的速度。
The theoretical basis of ANN is function approximation, it USES a two - level feedforward neural network to approach arbitrary function to realize better power flow control.
径向基函数神经网络的理论基础是函数逼近，用一个两层的前向网络去逼近任意函数，以更好地进行潮流控制。
According to the Markov approximation under a long haul condition, we get the inter-correlation function, log-amplitude and phase covariance function.
通过长程情况下的马尔科夫近似，得到了互相关函数，对数振幅和相位协方差函数。
Finally, the proposed method is applied to the problem of nonlinear function approximation.
最后将所提出的方法用于解决非线性函数的逼近问题。
It is to select product samples, then to use methods of function approximation and set up model.
首先选取产品样本，然后采用函数逼近方法建立评价模型。
In the end, wavelet neural network after being trained is used to approximation of function to performance good approximation of function.
最后用训练后得到的小波神经网络用于函数近似，体现小波神经网络良好的近似功能。
Secondly, in conditions of non-steady flow, azinuthal velocity and shear stress distribution were deduced according to function approximation method.
在非稳流下，采用函数拟合法，得出流体切向速度随半径变化的表达式及流体所受切向剪切力和分布曲线。
The RPA approximation is used to calculate the dielectric function, to derive the expression for the superconducting transition temperature and to discuss the effect of hybrid pairs.
对系统的介电函数作了RPA近似计算，得到了超导转变温度的表达式，并讨论了混合对效应。
For the problem that the input and output of real systems is a continuous process relative to time, this paper proposed a process neural network model for continuous function approximation.
针对实际系统的输入输出是与时间有关的连续过程，提出了一类用于连续过程逼近的过程神经元网络模型。
The algorithm is applied to XOR problem and nonlinear function approximation. Simulation results show that the chaos-BP algorithm needs shorter learning time than that of the standard BP and fast BP.
采用混合算法对XOR问题和非线性函数进行仿真，结果表明该算法明显优于标准BP算法和快速BP算法。
Car used to enhance learning (Q learning), using neural network Q function approximation.
小车采用加强学习(Qlearning)，采用神经网络对Q函数逼近。
Car used to enhance learning (Q learning), using neural network Q function approximation.
小车采用加强学习(Qlearning)，采用神经网络对Q函数逼近。
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