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。
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.
小波神经网络可以看作是以小波函数为基底的一种函数连接型网络，也可以认为是径向基函数(RBF)网络的推广。
Wavelet neural networks can be regard as not only the function-linked networks based wavelet function, but also the extension of Radical basis function (RBF) networks.
径向基函数神经网络是一种拓扑结构简单、学习过程透明的神经网络模型。
Radial Basis Function Neural Network is a kind of Neural Networks which have simple topological structure and clear learn procedure.
径向基函数神经网络是其中的一类非常有效的前馈网络，具有收敛速度快、逼近精度高、可避免局部最小等优越性。
Radial Basis Function Neural network is an effective feedforward network. It has high convergence rate and high approaching precision, and can avoid local optima.
在径向基函数神经网络中，隐层中心的数量和位置的选择是整个网络性能优劣的关键，直接影响网络的分类能力。
The choice of quantity and position of hidden layer radial basis functions is very important and directly affects the goodness of fit of overall network classification ability.
在径向基函数神经网络中，隐层中心的数量和位置的选择是整个网络性能优劣的关键，直接影响网络的分类能力。
The choice of quantity and position of hidden layer radial basis functions is very important and directly affects the goodness of fit of overall network classification ability.
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