在比较常用的几种电价预测方法的优缺点后,作者采用径向基函数神经网络(radial basis function neural networks,RBF)建立短期边际电价预测模型,用递阶遗传算法(HGA)同时训练RBF网络结构和参数.
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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.
采用非线性反馈控制电流内环，用RBF(径向基函数)神经网络设计了神经网络控制器控制输出电压外环。
Employing a RBF (radical basis function) neural network, a neural network controller is proposed for the output voltage control of the Buck - Boost converter.
用高斯径向基函数(RBF)神经网络逼近对象未知非线性，用高增益观测器估计系统不可测量状态。
Gaussian based radial basis function (RBF) neural networks are used to approximate the plant's unknown nonlinearities, and a high-gain observer is used to estimate the unmeasured states of the system.
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