基于RBF神经网络的非线性系统的预测

对于非线性系统的预测辨识，提出用动态节点生成构造性RBF神经网络作为预测模型，且RBF神经网络的学习算法采用一种新的全监督式学习算法，即神经网络隐层引入新节点时，通过使新节点的输出尽可能逼近残差序列的方向来获取网络参数，从而减少学习误差，使网络输出能够较好的跟踪系统输出。仿真表明该学习算法的有效性。

Prediction of Nonlinear Systems Based on RBF Neural Networks

Prediction identification for nonlinear systems is proposed,dynamic node creating training RBF neural network is adopted for predictive model,and the training algorithm of RBF neural network is a new training algorithm.During the training process,a new is created one by one to compensate the training error.The parameters of the new node are obtained by approximating the direction of new node' s output to that of the training error so that network output can track system output well.Simulations indicate the efficiency of the new algorithm.