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1·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。

2·This article indicates the principle for ANN bus protection based on function approximation ability, analyzes the functional relation of bus-bar object and builds the ANN model of bus-bar protection.

叙述了基于ANN函数逼近能力的母线保护原理，分析了母线保护物理对象的函数关系，构建了母线保护的人工神经网络模型。

3·At paraxial approximation the maximum of the matching function is increasing with increased divergence Angle. For different brightness of pumping light, the optimum value of the Angle is obtained.

发现在旁轴近似下，匹配函数的最大值随泵浦光发散角的增大而增大，在考虑到像散的影响后，得到泵浦光发散角的参考值。

4·The optimal approximation functions, of a continuous function which are constituted of the linear combination of series of Chebychev polynome have the characteristics of uniform approximation.

应用第一多项式系列的线性组合构成的某连续函数的最佳逼近函数，具有一致逼近的性质。

5·Study on approximation ability of nonlinear function.

模糊系统对非线性函数逼近能力的研究。

6·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。

7·Function approximation is one of the most important ability of ANN, a function object can be replaced by an ANN model with function approximation ability.

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。

8·The RBF network function approximation theory and method are introduced, and the method of nonlinear error correction of sensor is presented based on generalized regression neural network(GRNN).

介绍了径向基函数网络的函数逼近原理和方法，提出了一种基于广义回归神经网络（GRNN）的传感器非线性误差校正方法。

9·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。

10·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。