其中较为常用的是径向基函数神经网络(Radial Basis Function Neural Network,RBFNN),具有结构简单、泛化能力强和收敛速度快等特点。
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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.
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