文章用径向基神经网络设计内模控制系统。
This paper designs a internal model control system with radial basis function neural networks.
结果表明，用径向基神经网络预测股价是可行的和有效的。
Results of prediction experiments with real data prove the efficiency of our prediction method based on Radial Basis Function neural network.
最后，用径向基神经网络原理对载机导引器的优化作了研究。
Lastly, the optimized proportional navigation of modern fighter is researched with radial basis function (RBF).
用实际观测数据对该模型进行了试验，结果表明，用径向基神经网络转换GPS高程精度高于二次拟合法和BP神经网络法。
The model was tested with observed data. The results showed that RBF Neural Network conversion accuracy than Quadratic fitting and BP Neural Network.
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.
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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