RBF neural network provides an effective means for system identification and modeling with its advantages of smaller calculation quantity and high learning speed.
RBF神经网络以其计算量小，学习速度快，不易陷入局部极小等诸多优点为系统辨识与建模提供了一种有效的手段。
Using RBF NN can restore the airplane to the normal state by online regulating the effect of the uncertainties and the error caused by fuzzy modeling.
采用RBF神经网络在线补偿不确定项和模糊建模误差，能够使飞机获得满意的控制效果。
Simulation results indicate that the modeling method by using the RBF neural network identification technique is effective with the established model featuring a relative high precision.
仿真结果表明采用RBF神经网络辨识建模的方法是有效的，建立的模型精度较高。
In this paper, a new approach to sensor modeling based on RBF neural network is described. The construction of the neural network is simple and astringency is effective.
介绍了基于RBF神经网络的传感器建模新方法，其网络结构简单、收敛性好。
Through comparing with BP networks performance, we found that RBF networks achieve better result in and are suitable for modeling a non-linear system such as EMG.
通过实验比较 ，径向基函数网络较反向传播算法网络更适合于肌电信号这类非线性系统建模。
The validity and accuracy of modeling are tested by simulations, and the simulation results of the comparison between RBF neural networks and BP neural networks identification are given.
应用仿真对建模的有效性和精度进行了检验，并与BP神经网络辨识的效果进行了对比。
When applied to cracker modeling, MEP-GRNN shows advantage for non-linear molding over RBF-PLS approach. It has good prediction accuracy and stability.
该模型用于渣油裂解建模时，其预报精度和稳定性比r BF -PLS等方法均有所提高，表现了MEP - GRNN为非线性过程建模的优势。
When applied to cracker modeling, MEP-GRNN shows advantage for non-linear molding over RBF-PLS approach. It has good prediction accuracy and stability.
该模型用于渣油裂解建模时，其预报精度和稳定性比r BF -PLS等方法均有所提高，表现了MEP - GRNN为非线性过程建模的优势。
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