On the basis of macroscopic dynamic traffic flow model which is frequently used in traffic control, Radial basis Function (RBF) neural network is designed.
根据常用的高速公路交通流宏观动态模型，建立了高速公路交通流的RBF神经网络模型。
Then, aiming at the existing problem, the algorithm of dynamic recurrent neural network, RBF neural network and adaptive inverse control is studied in the paper.
接着，结合其存在的问题，对动态递归神经网络、R BF神经网络和自适应逆控制进行了算法研究。
A new method was represented to model dynamic linear regression system driven by data, in which a bayesian network was combined with the RBF neural network.
结合贝叶斯网络和神经网络，提出了一种建立数据驱动型的动态线性回归系统模型的方法。
Based on BP and RBF neural network model, the dynamic error of experiment was modeled and forecasted.
基于BP、R BF神经网络模型对实验系统的动态误差进行建模和预测。
As for it, by improving learning algorithm of traditional RBF neural network, a new dynamic cluster-based self-generated method for hidden layer nodes is proposed.
对此，本文改进了R BF神经网络的学习算法，提出了一种基于聚类的动态自生成隐含层节点的思想。
The system identifier based on RBF neural network which applies nearest neighbor clustering algorithm realizes the identification of the inverse dynamic system model.
辨识器采用RBF神经网络结构和最近邻聚类算法，实现了对系统逆动力学模型的动态辨识。
The system identifier based on RBF neural network which applies nearest neighbor clustering algorithm realizes the identification of the inverse dynamic system model.
辨识器采用RBF神经网络结构和最近邻聚类算法，实现了对系统逆动力学模型的动态辨识。
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