基于RBF网络的混沌时间序列的建模与多步预测

Modeling and Multi-Step Prediction of Chaotic Time Series Based on RBF Neural Networks

提出将RBF神经网络应用于混沌时间序列的建模与预测中 ,设计了一个三层RBF网络结构 ,说明了RBF网络用于混沌时间序列建模和预测时的基本性质。仿真结果表明 ,RBF网络模型对混沌时间序列有比较强的拟合能力和比较高的一步及多步预测精度。采用RBF网络进行混沌时间序列的建模和预测能够取得比其它方法好得多的效果。

We present that RBF neural networks can be used in the modeling and prediction of chaotic time series. A three layers RBF network structure is designed and fundamental properties of RBF networks are clarified when they are used in the modeling and prediction of chaotic time series. Simulations show that RBF networks models have good fitness and high accuracy of single and multistep prediction to the chaotic time series. Using RBF networks, simulation results for modeling and prediction of chaotic time series are far better than the other methods.